Machine Learning Engineer III, FAR (Frontier AI & Robotics)
DESCRIPTION
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers like Pieter Abbeel, Rocky Duan, and Peter Chen to make breakthrough foundation models run at production scale. As a Senior Machine Learning Engineer embedded in our science team, you'll be instrumental in transforming cutting-edge research into high-performance production systems. You'll collaborate directly with scientists to optimize large-scale transformer architectures for robotics applications, leveraging your expertise in CUDA and TensorRT to achieve unprecedented inference efficiency at Amazon scale.
In this role, you'll balance deep technical optimization work with strategic input on model architecture decisions, ensuring our innovative robotics models are designed with performance in mind from the ground up. You'll leverage NVIDIA's acceleration stack and other compilation techniques to tackle ambitious performance targets, working at the intersection of large language models and real-world robotics applications.
Key job responsibilities
- Drive inference optimization strategies for large-scale foundation models using TensorRT, CUDA, and other NVIDIA tools
- Collaborate closely with scientists to influence model architectures for optimal hardware utilization
- Design and implement efficient compilation pipelines for complex transformer architectures
- Develop comprehensive benchmarking frameworks to measure and optimize model performance
- Build robust monitoring solutions to ensure reliable model serving at scale
- Explore and evaluate emerging optimization techniques including ONNX Runtime and other ML compilers
- Maintain high engineering standards through proper testing, documentation, and code review practices
A day in the life
- Optimize transformer blocks using custom CUDA kernels and TensorRT optimization techniques
- Partner with scientists to analyze model architectures and propose efficiency improvements
- Implement and benchmark various optimization strategies for large-scale models
- Debug performance bottlenecks using NVIDIA profiling tools
- Participate in technical discussions about new model architectures with the science team
- Design and maintain performance monitoring systems for production deployment
- Prototype new acceleration approaches using emerging compilation frameworks
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:
1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
About the team
At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team, led by pioneering AI researchers Pieter Abbeel, Rocky Duan, and Peter Chen, is building the future of intelligent robotics through groundbreaking foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios.
What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations.
Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
BASIC QUALIFICATIONS
- Bachelor's degree in computer science or equivalent
- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- Strong expertise in Python, C++ and CUDA programming
- Experience with TensorRT or similar ML optimization frameworks
- Track record of optimizing ML models for production
PREFERRED QUALIFICATIONS
- Expertise in NVIDIA's ML stack (cuDNN, CUDA Graph, etc.)
- Experience with ML compilers (ONNX Runtime, TVM, etc.)
- Experience with transformer model optimization
- Background in performance profiling and optimization
- Experience working directly with research teams
- Track record of building robust monitoring systems
- Experience with large-scale ML serving systems
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $151,300/year in our lowest geographic market up to $261,500/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.