CVPR 2026 Career Opportunities

Alexa International Science team is looking for a passionate, talented, and inventive Senior Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. At this level, you will drive cross-team scientific strategy, influence partner teams, and deliver solutions that have broad impact across Alexa's international products and services.

Key job responsibilities As a Senior Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs, particularly delivering industry-leading scientific research and applied AI for multi-lingual applications — a challenging area for the industry globally. Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains. The ideal candidate possesses a solid understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environment, like to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and are able to influence and align multiple teams around a shared scientific vision. Basic Qualifications

• PhD, or Master's degree
• 4+ years of applied research experience
• Experience leading the design, build and deployment of complex and performant (reliable and scalable) software solutions in production
• PhD in CS, CE, ML, or related field with 4+ years of relevant post-PhD research experience; or Master's degree with 7+ years of equivalent experience

• Deep expertise in state-of-the-art LLM architectures, training, evaluation, and post-training techniques (SFT, DPO, RLHF, RLAIF) Preferred Qualifications

• Experience in building speech recognition, machine translation and natural language processing systems

• Experience in patents or publications at top-tier peer-reviewed conferences or journals

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

Location: Beijing

Responsibilities 1. Design and develop AI Agents, covering the perception‑decision‑action loop, multi‑agent collaboration, long‑term memory, and reasoning mechanisms. 2. Research cutting‑edge paradigms such as ReAct, AutoGPT, and CoT; master agent development frameworks including LangChain, AutoGen, and CrewAI. 3. Optimize core agent capabilities powered by LLMs, including planning, tool use, reflection, and code generation. 4. Drive large‑scale application of agents across product scenarios such as search, dialogue, productivity tools, data platforms, cloud storage/document editing, and lifestyle & entertainment. 5. Explore integrated RAG‑Agent architectures to enhance the synergy between knowledge understanding and action capabilities. 6. Build evaluation systems for agents, continuously improving success rates, latency, cost, and user experience.

Requirements 1. Master’s degree or above in Computer Science, Artificial Intelligence, or a related field. 2. Familiar with the principles and applications of large language models (LLMs), with hands‑on experience in Prompt Engineering and Fine‑tuning. 3. Proficient in at least one agent development framework, such as LangChain, LlamaIndex, or AutoGen. 4. Experience with RAG, knowledge base construction, or multi‑agent collaborative development is a strong plus. 5. Familiarity with reinforcement learning (e.g., PPO, DPO) or planning algorithms (e.g., A*, MCTS) is preferred. 6. Strong engineering skills with the ability to rapidly turn algorithms into production‑ready systems. 7. Publications in top‑tier conferences or contributions to open‑source agent projects are highly valued.

Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models.

We are seeking an exceptional Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases.

Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Drive technical direction for specific research initiatives, ensuring robust performance in production environments.

A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Collaborate with fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Collaborate with fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Contribute to focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Make significant hands-on contribution to technical solutions

About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance.

Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

United States, California, Santa Clara

Want to join a fun, creative company that is on the cutting edge of outstanding technologies? NVIDIA is developing groundbreaking solutions in some of the most exciting technology areas globally, including Virtual Reality, Artificial Intelligence, Deep Learning and Autonomous Vehicles.

NVIDIA's AV Eval team is building the next generation of driving behavior evaluation — moving beyond hand-crafted rules to learned evaluation using LLMs, VLMs, and agentic workflows. You'll define how we measure whether an autonomous vehicle drives well, building systems that bridge ML research and production evaluation. You'll ship systems that run at scale on real-world driving data and produce metrics that block or green-light software releases. In this role you will get to work on next-gen AV evaluation and create a direct impact on vehicle safety and shipping decisions. Join a new team being built from scratch — high ownership, high visibility to NVIDIA AV leadership.

What You will be doing: - Design and build learned evaluation pipelines that assess driving behavior using LLMs, VLMs, and multimodal models - Develop agentic workflows that chain model inference, retrieval, and structured reasoning to evaluate complex driving scenarios - Define evaluation-of-evaluation methodology — how do we know our learned evaluators are correct? - Build golden-set frameworks and calibration loops for learned metrics - Partner with AML (Alpamayo Logos) teams on model-specific eval needs (e.g., COT prediction quality, AML regression coverage) - Instrument evaluation systems with robust experiment tracking, A/B comparison tooling, and model versioning - Contribute to the team's transition from rule-based to learned evaluation: identify metrics and analyzers that are candidates for ML replacement and build the alternatives

What we need to see: - PhD (4+ years), MS (6+ years), or BS (or similar) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field. - Hands-on experience building LLM/VLM-based pipelines — fine-tuning, prompt engineering, retrieval-augmented generation, chain-of-thought - Track record of shipping ML systems to production (not just prototyping or publishing) - Strong software engineering fundamentals — writing clean, tested, reviewable code in Python and C++ - Experience with evaluation methodology: precision/recall, inter-rater reliability, calibration, annotation pipelines - Comfort with large-scale data processing (Spark, Dask, or similar) - Strong Python skills. Experience with PyTorch or JAX. Comfortable with GPU-based training workflows.

Ways to stand out: - Autonomous driving, robotics, or safety-critical domain experience - Familiarity with driving behavior taxonomies (cut-ins, hard braking events, lane-keeping metrics, scenario-based evaluation) - Experience with video understanding models or multi-modal evaluation. Knowledge of agentic AI frameworks (LangChain, DSPy, CrewAI, or custom) - Track record of influencing technical direction across team boundaries - Experience with LLM/VLM fine-tuning or application development

At NVIDIA, we’re dedicated to making self-driving vehicles a reality and believe this technology can save millions of lives. Join a team of innovative thinkers at one of the world’s most respected technology companies. If you’re motivated, curious, and ready to make a difference, we’d love to meet you! We believe that building self-driving vehicles will be a defining contribution of our generation (e.g. traffic accidents are responsible for ~1.25 million deaths per year world-wide). We have the funding and scale, but we need your help on our team. NVIDIA is widely considered to be one of the technology world’s most desirable employers with some of the most forward-thinking people in the world working here. If you're entrepreneurial and autonomous, we want to hear from you!

The Product: AWS Machine Learning accelerators are at the forefront of AWS innovation and one of several AWS tools used for building Generative AI on AWS. The Inferentia chip delivers best-in-class ML inference performance at the lowest cost in cloud. Trainium will deliver the best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by cutting edge software stack, the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, runtime and natively integrates into popular ML frameworks, such as PyTorch, TensorFlow and MxNet. AWS Neuron and Inferentia are used at scale with customers like Snap, Autodesk, Amazon Alexa, Amazon Rekognition and more customers in various other segments.

The Team: As a whole, the Amazon Annapurna Labs team is responsible for silicon development at AWS. The team covers multiple disciplines including silicon engineering, hardware design and verification, software and operations.

The AWS Neuron team works to optimize the performance of complex neural net models on our custom-built AWS hardware. More specifically, the AWS Neuron team is developing a deep learning compiler stack that takes neural network descriptions created in frameworks such as TensorFlow, PyTorch, and MXNET, and converts them into code suitable for execution. As you might expect, the team is comprised of some of the brightest minds in the engineering, research, and product communities, focused on the ambitious goal of creating a toolchain that will provide a quantum leap in performance.

You: As a Sr. Machine Learning Compiler Engineer III on the AWS Neuron team, you will be a thought leader supporting the ground-up development and scaling of a compiler to handle the world's largest ML workloads. Architecting and implementing business-critical features, publish cutting-edge research, and mentoring a brilliant team of experienced engineers excites and challenges you. You will leverage your technical communications skill as a hands-on partner to AWS ML services teams and you will be involved in pre-silicon design, bringing new products/features to market, and many other exciting projects. A background in Machine Learning and AI accelerators is preferred, but not required.

In order to be considered for this role, candidates must be currently located or willing to relocate to Seattle.

About the team Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.

Sunnyvale, CA

Role Description: We are seeking a Lead Mobile Software Engineer to help lead development for Matterport's 3D technology. We are working with OEM and hardware technology partners to provide cutting-edge capture experiences on next-generation mobile and camera devices.

We are looking for someone who is passionate about building great mobile software and is motivated to take the lead in the creation of mobile apps for capturing, editing, and viewing 3D spaces and objects.

Responsibilities: - Collaborate closely with the engineering team and product marketing to define, design, develop, and deliver new exciting features on Matterport’s applications - Continuously explore, evaluate, and incorporate new technology into our products and processes to improve development efficiency - Deliver a quality software solution using the Agile mobile development cycle - Analyze business requirements, provide development estimates, feedback and proper implementation - Develop, maintain, support, troubleshoot, monitor and optimize existing mobile applications - Research and recommend new mobile tools and applications

Basic Qualifications: - Bachelor’s degree in Computer Science, Electrical Engineering or a related field, from an accredited, not-for-profit University or College, or equivalent experience. - A track record of commitment to prior employers. - 5+ years experience in high-performance software development - 3+ experience in Android mobile development experience with deep technical knowledge of mobile frameworks and how to build reactive UI design - Experience working with RESTful web APIs

Sunnyvale, CA

About the Role: As a Senior MLOps Engineer at Matterport, a part of CoStar Group, you will be pivotal in enhancing the performance, efficiency, and scalability of our machine learning models. You will be responsible for identifying bottlenecks, applying advanced optimization techniques, and deploying highly efficient models into production. You will work closely with ML R&D Engineers and other engineering teams to analyze model performance, optimize inference speed and resource utilization, and ensure the seamless integration of optimized models into our spatial computing platform. This role requires a strong understanding of machine learning principles, expertise in model optimization techniques, and a passion for pushing the boundaries of what's possible with efficient ML deployment. You will contribute to a product that is revolutionizing how people interact with and understand real estate by ensuring our models are robust, fast, and deliver exceptional user experiences.

What you will do: - Analyze and profile machine learning models to identify performance bottlenecks and areas for optimization. - Implement and apply model optimization techniques such as quantization, pruning, distillation, and neural architecture search to improve inference speed and reduce resource consumption. - Develop and integrate specialized libraries and tools for efficient model execution on various hardware platforms (e.g., GPUs, CPUs, edge devices). - Collaborate with ML R&D Engineers to understand model architectures, training procedures, and deployment requirements. - Design and conduct experiments to measure the impact of optimization techniques on model performance and accuracy. - Automate model optimization workflows and build robust continuous integration/continuous deployment (CI/CD) pipelines for optimized models. - Stay up-to-date with the latest research and industry trends in ML model optimization, hardware acceleration, and efficient AI. - Contribute to the continuous improvement of MLOps practices and infrastructure for model deployment and monitoring. - Ensure the scalability and reliability of optimized models in production environments.

Basic Qualifications: - Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field, or equivalent practical experience. - 3+ years of experience in machine learning engineering, with a focus on model optimization and deployment. - Proficiency in Python and strong programming skills. - Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and optimization libraries. - Solid understanding of machine learning algorithms, model architectures, and deep learning concepts. - Experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments. - Familiarity with version control systems (e.g., Git) and agile development methodologies. - Excellent problem-solving skills and attention to detail, particularly in model performance and accuracy. - Strong verbal and written communication skills.

Preferred Qualifications: - Master's degree in Computer Science, Data Science, or a related quantitative field. - 5+ years of industry experience in ML Model Optimization, ML Engineering, or MLOps, particularly with large-scale 2D/3D computer vision models. - Experience with hardware-aware model optimization and deployment to edge devices. - Knowledge of model compression techniques and their practical application. - Experience with workflow orchestration tools (e.g. Temporal, Airflow, Kubeflow). - Familiarity with containerization technologies (e.g., Docker, Kubernetes). - Demonstrated ability to build and maintain robust, scalable, and automated ML model deployment pipelines. - Experience working in a fast-paced R&D environment. - Excellent communication skills, both written and verbal, with the ability to articulate complex technical concepts to diverse audiences.

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models.

We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence.

We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities

The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment.

Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration.

As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments.

Key job responsibilities - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.

VinUniversity (VinUni) is Vietnam’s first private, non-profit university established to international standards, aiming to become a world-class institution and transform higher education in Vietnam. VinUni collaborates strategically with Cornell University and University of Pennsylvania to advance excellence in research, teaching, and innovation. The university includes the College of Arts and Sciences, College of Business and Management, College of Engineering and Computer Science (CECS), and College of Health Sciences.

Supported by Vingroup, one of Asia’s leading private corporations, VinUni benefits from strong industry connections in technology, real estate, infrastructure, green energy, and social enterprises. Students and faculty can engage with companies such as VinFast, Vinpearl, Vinmec, Vinschool, and Vinhomes, as well as innovation initiatives including VinVenture, VinMotion, VinRobotics, and VinSpace.

In September 2024, VinUni became the youngest and fastest university worldwide to achieve QS 5 Stars in nine categories, including teaching, employability, governance, facilities, and social impact. In October 2024, UNESCO appointed VinUni as its first and only University Chair focusing on Environmental Leadership, Cultural Heritage, and Biodiversity.

As part of its ambition to become a top 100 global university, VinUni is investing heavily in advanced research centers, facilities, and transformative technologies. Major research areas include AI/Machine Learning, Data Science, Environmental Intelligence, Health Science Innovation, Policy Development, and Sustainable Societies.

The Faculty of Electrical Engineering position is a full-time, research-focused role within CECS, with opportunities to collaborate with the Environmental Intelligence Research Cluster. Research areas include advanced engineering and intelligent systems, wireless charging for electric vehicles, AI-enabled digital twin energy systems, intelligent power electronics, adaptive control, autonomous robotics and vehicles, IC design, semiconductors, and IoT technologies.

CECS provides a strong interdisciplinary and application-oriented academic environment through partnerships with seven research centers and extensive collaboration within the Vingroup ecosystem. The Environmental Intelligence Research Cluster includes the Center for Material Intelligence and Technology (CMIT) and the Center for Environmental Intelligence (CEI). CMIT focuses on advanced materials research for electronics, energy, biomedicine, and sustainability, while CEI applies interdisciplinary research and advanced technologies to solve environmental challenges and promote sustainable development. Research directions also include lithium-ion battery recycling, sodium-ion batteries, triboelectric nanogenerators, green hydrogen, seawater electrolysis, ammonia production, and fuel cell catalysts.

Faculty responsibilities cover research, teaching, and service. Research duties include leading high-impact projects, publishing in top-tier journals, securing external funding, supporting interdisciplinary collaboration, and contributing to innovation and technology transfer. Teaching responsibilities involve curriculum development, delivering lectures and tutorials, assessing students, supervising projects and research students, and mentoring junior faculty. Service duties include committee work, faculty recruitment, accreditation, program development, and supporting VinUni Vision 2030 initiatives.

Applicants must hold a PhD from a prestigious university in Electrical Engineering, Power Electronics, Control Engineering, Mechatronics, Robotics, Computer Engineering, Electronics Engineering, Embedded Systems, or related fields. Candidates should demonstrate strong teaching capability, student-centered learning approaches, and evidence of research excellence through publications, international collaboration, and potential to build externally funded research programs. Experience in multinat

USA, California, Santa Clara

At NVIDIA, we are seeking exceptional engineers to join our autonomous driving team to design, implement, and deploy cutting-edge end-to-end autonomous driving systems, running on NVIDIA chips in mass-production vehicles. Our strategy has evolved from AI 1.0 — building a driver from scratch — to AI 2.0 — teaching an intelligent agent to drive. This next phase leverages LLMs, VLMs, and VLAs to bring unprecedented reasoning, planning capabilities, and interactivity with the driving system to autonomous vehicles and general robotics. Let’s build the future of autonomy—together!

What You’ll Be Doing: - Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems. - Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications. - Explore novel data generation and collection strategies to improve diversity and quality of training datasets. - Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met. - Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.

What We Need to See: - Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems. - Deep understanding of modern deep learning architectures and optimization techniques. - Proven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale. - Strong programming skills in Python and proficiency with major deep learning frameworks. - Familiarity with C++ for model deployment and integration in safety-critical systems. - PhD with 4+ years, MS (or equivalent experience) with 6+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.

Ways to Stand Out from the Crowd: - Experience with LLM/VLM/VLA systems deployable to autonomous vehicles or general robotics. - Publications, open-source contributions, or competition wins related to LLM/VLM/VLA systems. - Deep understanding of behavior and motion planning in real-world AV applications. - Experience building and training large-scale datasets and models. - Proven ability to optimize algorithms for real-time performance in resource-constrained environments and strong track record of taking projects from concept to production deployment.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.