Senior Applied Scientist, Amazon Industrial Robotics
AI in this role
As an Applied Scientist III, you will develop and improve machine learning systems that enable robots to learn from deployed fleet experience and continuously improve performance. You will leverage state-of-the-art ML techniques, evaluate them against representative robotics tasks and operational scenarios, and adapt them to meet the robustness, reliability, and performance needs of production environments. You will invent new algorithms where gaps exist. You'll collaborate closely with robotics teams, software engineering, manufacturing optimization, and operations teams, and your outputs will directly power the systems that enable robots to get smarter over time.
The ideal candidate brings deep expertise in machine learning and large-scale data systems, with a proven track record of delivering scientifically complex solutions into production. You are hands-on, writing significant portions of critical-path scientific code while driving your team's scientific agenda. If you're passionate about building the intelligent systems that enable robots to learn and improve from every task they perform, this role offers the chance to make a lasting impact on the future of automation.
Key job responsibilities
- Identify and devise new scientific approaches for continuous learning, fleet optimization, predictive analytics, and performance intelligence when the problem is ill-defined and new methodologies need to be invented
- Lead the design, implementation, and successful delivery of scientifically complex solutions for continuous learning pipelines, fleet optimization, and predictive maintenance in production
- Design and build ML models including reinforcement learning training infrastructure, anomaly detection systems, predictive maintenance models, and fleet optimization algorithms
- Write a significant portion of critical-path scientific code with solutions that are inventive, maintainable, scalable, and extensible
- Execute rapid, rigorous experimentation with reproducible results, closing the gap between simulation and real-world robotics environments
- Build evaluation benchmarks that measure model performance against operational outcomes including fleet reliability, prediction accuracy, and learning velocity rather than traditional ML metrics alone
- Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities
- Partner with robotics teams, manufacturing optimization, and fleet systems teams to ensure scientific approaches are grounded in operational reality
- Drive your team's scientific agenda and role model publishing of research results at peer-reviewed venues when appropriate and not precluded by business considerations
- Actively participate in hiring and mentor other scientists, improving their skills and ability to deliver
- Write clear narratives and documentation describing scientific solutions and design choices
Basic qualifications
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 12+ years of building machine learning models or developing algorithms for business application experience
- 5+ years of practical work applying ML to solve complex problems experience
- Experience in several of the following areas: machine learning, statistics, deep learning, natural language processing, or information retrieval
- Demonstrated technical contributions through publications, patents, or impactful production systems
Preferred qualifications
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Experience building large-scale machine learning and AI solutions at Internet scale
- Experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda, or experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Experience statistical modeling, or related analytic techniques
- Experience in leading teams for developing natural language processing or dialog management systems (like commercial speech products or government speech projects)
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.
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
How we score this
Senior Applied Scientist, Amazon Industrial Robotics at Amazon scores 98 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 4. Building AI systems is the job itself: without AI, the role would not exist.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Questions you could be asked
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Walk me through how you've used TensorFlow in your day-to-day work.
- What are the limits of scikit-learn that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
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- List these exact terms on your resume: Nlp, TensorFlow, and scikit-learn. An applicant tracking system matches the wording, not the idea.
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- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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