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About This Role
DESCRIPTION
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At Frontier AI \& Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier 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 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.
As a Senior Research Engineer embedded in our science team, you'll be instrumental in transforming innovative research into high\-performance production systems. You'll collaborate directly with scientists to build and optimize large\-scale transformer models for robotics applications.
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 PyTorch and 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
- Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for large\-scale training workloads.
- Develop high\-performance optimizations to maximize throughput and efficiency.
- Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.
- Establish standards for reliability, maintainability, and security, ensuring systems are robust under rapid iteration.
- Collaborate with researchers to influence model architectures for optimal hardware utilization
- Develop comprehensive benchmarking frameworks to measure and optimize model performance
A day in the life
In this role, you will:
- 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
- Manage pre/post training runs and continue improve system stability and throughput
- Prototype new acceleration approaches using emerging compilation frameworks
BASIC QUALIFICATIONS
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- 5\+ years of non\-internship professional software development experience, or Bachelor's degree in computer science or equivalent
- 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
- Bachelor's degree in computer science or equivalent
- Expertise in Python, PyTorch, and CUDA programming
- Experience with TensorRT or similar ML optimization frameworks
- Ability to optimize ML models for production
- Experience working directly with research teams
- Experience working on distributed training for the foundation model to make them stable, reliable, and performant
PREFERRED QUALIFICATIONS
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- 5\+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Track record of improving research productivity through infrastructure design or process improvements.
- Experience in ML infrastructure such as Pytorch, Megatron, TorchTitan, etc
- Experience with ML compilers (ONNX Runtime, TVM, etc.)
- Experience with transformer model optimization or RL framework development
- Background in performance profiling and optimization
- Track record of building robust monitoring systems
- Experience with large\-scale ML serving systems
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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 U.S. geographic markets. The base pay for this position ranges from $150,000/year in our lowest geographic market up to $300,000/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. Applicants should apply via our internal or external career site.
Salary Context
This $150K-$300K range is above the 75th percentile for Research Engineer roles in our dataset (median: $185K across 51 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,824 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $260,000 based on 401 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($225K) sits 13% below the category median. Disclosed range: $150K to $300K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 98 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, Data Scientist. Positions span New York, NY, US, Seattle, WA, US, Sunnyvale, CA, US. Compensation range: $101K - $300K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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