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DESCRIPTION
At Amazon, we obsess over our customers and maintaining their trust. To earn that trust in an environment as vast and varied as Amazon's requires the applied skills of experienced and innovative Technical Program Managers to tackle challenges at Amazon scale.
We are seeking a Sr. Technical Program Manager to lead AI Native Transformation efforts on the Engine team. Engine delivers platform tooling that allows all Amazon employees to innovate, from capacity management, availability and incident management, and infrastructure hosting, to 1P and 3P productivity tooling, corporate access, foundational AI platforms, and the hardware Amazonians use to do their jobs. We are looking for an individual who can lead technical initiatives, build and maintain strategic relationships with internal and external partners, define and implement repeatable processes, and deliver results across a complex landscape. If you are passionate about building processes and technology that directly impact all Amazonians and want to join a team shaping the future of AI Native delivery at Amazon, we want to talk to you.
You will own the mechanisms that turn our AI enablement strategy into adoption across Engine. You will partner with a Principal Engineer to identify best practices, experiment with frontier teams, and roll mature practices out to every Engine team. You will run the monthly AI Native business review, own the retrospective cycle that sharpens our mechanisms, curate central resources (agent templates, shared skill packs, adoption playbooks), and influence teams to deliver productivity tooling that supports AI Native workflows. Success takes maturity, high judgment, negotiation, influence, analytical talent, and leadership through ambiguity.
You will work across teams and through ambiguity. You thrive by building consensus and driving teams toward results. You will anticipate bottlenecks, provide escalation management, make trade-offs, and balance business needs against technical constraints. An ability to take large, complex programs and break them down into manageable pieces to deliver them on time is essential. Maturity, high judgment, negotiation skills, ability to influence, analytical talent, and leadership are critical to success in this role.
Key job responsibilities
- Own the planning, execution, and delivery of the AI Native Transformation program across Engine, from experiment through org-wide rollout. Partner with the Principal Engineer to identify best practices, define the mechanisms that drive adoption, establish milestones, identify critical-path dependencies, and drive teams to deliver on schedule.
- Run the monthly business review for leadership: define metrics, report on progress against goals, and frame the decisions leadership needs to make.
- Identify common knowledge-worker workflows, rebuild them AI-natively, and influence the teams that build Amazon's productivity tooling to deliver those solutions as product features, extending Engine's work to every Amazonian.
- Coordinate across engineering teams, central tooling partners, and enablement teams to align priorities, resolve cross-organizational blockers, and build lasting partnerships with stakeholders at all levels.
- Curate the central resources teams rely on, including default agent templates, shared skill packs, and adoption playbooks.
- Program the organization's AI upskilling, working with the enablement team and role leads to schedule trainings, workshops, and office hours.
- Run the retrospective cycle each period, turning what teams report and what the metrics show into mechanism changes that raise adoption over time.
About the team
Our customers are Amazonians, every one of them. We build the technology that keeps the whole company productive, from the systems that keep Amazon's websites running to the productivity tools, corporate access, AI platforms, and hardware people use every day. Our mission is to foster invention for all Amazonians, and right now we are making that work AI-native. You would work closely with a principal engineer to define best practices, with builder teams to transform how they work, and with the teams who own productivity tooling to shape the features that bring AI-native workflows to every Amazonian.BASIC QUALIFICATIONS
- 5+ years of technical product or program management experience
- 7+ years of working directly with engineering teams experience
- 3+ years of software development experience
- 5+ years of technical program management working directly with software engineering teams experience
- Experience managing programs across cross functional teams, building processes and coordinating release schedules
PREFERRED QUALIFICATIONS
- 5+ years of project management disciplines including scope, schedule, budget, quality, along with risk and critical path management experience
- Experience managing projects across cross functional teams, building sustainable processes and coordinating release schedules
- Experience defining KPI's/SLA's used to drive multi-million dollar businesses and reporting to senior leadership
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, NY, New York - 163,600.00 - 221,300.00 USD annually
USA, WA, Seattle - 148,700.00 - 201,200.00 USD annually
Salary Context
This $148K-$221K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% 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 $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $148K to $221K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Amazon.com AI Hiring
Amazon.com has 68 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Seattle, WA, US, San Diego, CA, US. Compensation range: $148K - $327K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 tracked positions. That's 4% above 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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