As a Senior Applied Scientist you sit directly with customers and partners. You help them envision complex uses of AWS by building working prototypes of specific use cases, tailored to their data, devices, and systems. Engagements run in short build cycles, typically four to six weeks, and are designed to be innovative and evaluative, demonstrating the art of the possible on AWS while proving a credible path to production.
You will work across the areas where we are placing our biggest bets. These shift as the technology does, but today they include:
- Physical AI and autonomous robotics. World models, vision-language-action (VLA) models, cloud-based and agentic VLA, real-to-sim and sim-to-real pipelines, and agentic control of physical systems. We challenge the assumption that these models have to live only at the edge.
- Autonomous scientific discovery. Self-driving labs and instrument intelligence, where agents plan, run, and interpret experiments across fleets of scientific instruments.
- Agentic developer intelligence. Multi-agent systems, code agents, and automated research workflows that change how software gets built.
- Scaling what we learn. Turning one-off prototypes into reusable patterns and assets the broader field can pick up and run with.
You will not just apply these techniques. You will help decide which bets are worth making and prove or disprove them in the open with real customer problems.
Key job responsibilities
- As a key member of the AWS Specialists and Partner Prototyping team, ensure customer success through hands-on participation producing and demonstrating prototypes on the AWS platform.
- Participate in deep architectural and scientific design discussions to ensure prototypes are crafted for successful deployment in the cloud and a credible path to production.
- Bring applied science depth to engagements: frame the problem, select and adapt models, design experiments, and judge technical feasibility honestly, including when the answer is that an approach will not work.
- Conduct one-to-few and one-to-many training sessions to transfer knowledge to prototyping customers, partners, and internal business partners.
- Act as a thought leader for your area of domain expertise across AWS, and share best-practice knowledge among the community.
- Author or otherwise contribute to AWS customer-facing publications such as peer reviewed position papers, blogs, and reference patterns.
- Act as a technical liaison between customers, service engineering teams, and sales, carrying real-world signal back to the teams building the services.
A day in the life
No two engagements look the same, which is the point. A typical week might include:
- Running a working session with a customer's scientists and engineers to pressure-test an idea and agree on what a prototype needs to prove.
- Writing code and training or fine-tuning models in the lab, often against the customer's own data, devices, or robots.
- Pairing with a prototyping SA and engagement manager, the standard pod you deliver alongside, to keep the build moving and the scope honest.
- Demonstrating a working prototype live and walking stakeholders through what it does, what it does not yet do, and what production would take.
- Capturing what you learned as a reusable pattern or publication so the next team does not start from zero.
- Spending time on the horizon itself: reading, experimenting with a new model or technique, and forming a point of view on where a bet is heading.
You will move between hands-on building and customer-facing explanation constantly. The best people in this role are equally comfortable in a research paper, a code editor, and a room full of executives.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 5+ years of applied research experience
- Experience in building machine learning models for business application
- Experience programming in Java, C++, Python or related language
- Experience using managed ML/AI solutions
PREFERRED QUALIFICATIONS
- Experience conveying complex technical concepts to both technical and business audiences
- Experience working with Advanced Compute technologies including, but not limited to: Accelerated Compute, High Performance Compute, Visual/Spatial Compute, and/or IoT.
- Experience with vLLM, SGLang, TensorRT or similar platforms in production environments, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
- Experience working with Robotics; training and deploying Vision Language Action (VLA) models, World Action models in simulation environments as well as real
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.
USA, CA, Santa Clara - 192,200.00 - 260,000.00 USD annually
USA, NY, New York - 183,800.00 - 248,700.00 USD annually
USA, VA, Herndon - 167,100.00 - 226,100.00 USD annually
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
