Interested in this AI/ML Engineer role at Amazon.com?
Apply Now →About This Role
DESCRIPTION
---------------
Amazon Robotics (AR) is at the forefront of applying state\-of\-the\-art artificial intelligence to solve real\-world challenges at unprecedented scale. Our fulfilment centers handle more individual items than any company in the world, combining computer vision, mobile robots, advanced end\-of\-arm tooling, high\-degree\-of\-freedom movement, and state\-of\-the\-art AI. We collaborate with teams across Amazon worldwide to conceive, develop, prototype, and deploy intelligent robotic systems that push the boundaries of industrial automation.
Within Amazon Robotics, the Global Operations – Artificial Intelligence (GO\-AI) organization enables Amazon to accelerate and scale the next generation of AI\-powered robotic solutions. We're transforming how complex visual reasoning tasks are performed across Amazon's robotics operations, moving from traditional manual data annotation processes to state\-of\-the\-art foundation model solutions. GO\-AI's Technology \& Development team is a cross\-functional organization of Software Development Engineers, Technical Program Managers, Program Managers, and Business Intelligence Engineers responsible for GO\-AI's technical strategy, platform capabilities, and delivery.
As a Senior Technical Program Manager on GO\-AI's Technology \& Development team, you will partner with product, science, software engineering, data engineering, operations, and other Amazon Robotics teams to accelerate the evaluation, deployment, and scaling of AI solutions across Amazon Robotics. You will own complex, cross\-functional programs spanning solution definition, experimentation, operational readiness, deployment, and continuous improvement. Success in this role requires balancing technical depth with strong program ownership.
This is a rare opportunity to work with a team building AI systems that operate at Amazon scale while solving novel technical challenges in computer vision, natural language processing, and human\-AI collaboration. You'll collaborate with world\-class scientists, engineers, and operations teams to deploy state\-of\-the\-art research into production systems that deliver real\-world impact at global scale.
Key job responsibilities
- Partner with other Amazon teams to understand AI automation\-based use cases, evaluate partnership opportunities, and define GO\-AI's technical and operational plans to support them.
- Build trusted partnerships with business and technical teams, serving as the primary GO\-AI partner throughout the program lifecycle.
- Design and lead human\-in\-the\-loop solutions and AI workflows that bridge science, engineering, and operations.
- Partner with GO\-AI Software, Science, Data, and Business Intelligence Engineering teams to identify capability gaps, prioritize features, and influence long\-term platform roadmaps.
- Define success metrics, monitor program health, and use data to drive decisions, identify risks, and evaluate trade\-offs.
- Drive senior leadership reviews through clear written and verbal communication of program strategy, risks, trade\-offs, and recommendations.
- Drive process simplification by eliminating inefficiencies and building scalable mechanisms.
A day in the life
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
Within Amazon Robotics, the Global Operations \- Artificial Intelligence (GO\-AI) team enables Amazon to accelerate and scale the next generation of AI\-powered robotic solutions. We're transforming how complex visual reasoning tasks are performed across Amazon's robotics operations, moving from traditional manual data annotation processes to state\-of\-the\-art foundation model solutions.
BASIC QUALIFICATIONS
------------------------
- 6\+ years of technical product or program management experience
- 5\+ years of project management disciplines including scope, schedule, budget, quality, along with risk and critical path management experience
- Bachelor's degree in engineering, computer science or equivalent
- Experience defining technical requirements and partnering with engineering teams to deliver scalable technical solutions.
- Demonstrated ability to drive clarity and define program structure in ambiguous domains.
- Excellent verbal and written communication skills.
PREFERRED QUALIFICATIONS
----------------------------
- Experience with computer vision, human\-in\-the\-loop systems, or model evaluation and deployment.
- Experience influencing product or platform roadmaps across multiple engineering teams.
- Track record of coaching and mentoring other Technical Program Managers.
- Familiarity with robotics, fulfilment operations or large\-scale annotation/labelling workflows.
- Experience working with applied science teams and foundation model technologies.
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, MA, Boston \- 148,700\.00 \- 201,200\.00 USD annually
USA, MA, North Reading \- 148,700\.00 \- 201,200\.00 USD annually
USA, MA, Westboro \- 148,700\.00 \- 201,200\.00 USD annually
Salary Context
This $148K-$201K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($174K) sits 20% below the category median. Disclosed range: $148K to $201K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 97 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Sunnyvale, CA, US, Culver City, CA, US, San Francisco, CA, US. Compensation range: $97K - $327K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.