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About This Role
DESCRIPTION
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Annapurna Labs (our organization within AWS) designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago—even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world.
As a member of the Annapurna ML/AI Mechanical Thermal Engineering team, you'll own the end\-to\-end thermal and mechanical architecture for ML/AI accelerator platforms — from initial concept through production deployment and ongoing fleet operations. You'll design mechanical and cooling solutions for some of the highest power\-density silicon in the industry, balancing performance, reliability, cost, and operational efficiency at massive scale.
This is a technically challenging role requiring you to operate in ambiguity and at a fast pace. You'll collaborate across silicon design, electrical engineering, firmware, manufacturing, supply chain, and operations teams to deliver platforms that power AWS ML/AI services at global scale.
Key job responsibilities
As a Cloud Hardware Development Engineer (Thermal/Mechanical), you will:
Platform Ownership
Own the complete thermal and mechanical design for ML/AI accelerator platforms — from rack\-level infrastructure down to chip packaging, including mechanical packaging, structural integrity, and interconnect systems
Define thermal and mechanical design requirements, establish design targets, and drive cross\-functional alignment that enables parallel development across hardware, firmware, and software teams
Deliver production platforms through the full lifecycle: concept, design, analysis, prototyping, validation, manufacturing ramp, and fleet operations
Thermal \& Mechanical Design
Design and optimize cooling solutions (air and liquid) for high\-power\-density ML/AI accelerators
Develop detailed CFD models, compact RC models, and structural FEA for SoC/package thermal analysis and mechanical integrity
Design rack manifolds, cold plates, and data center liquid cooling interfaces for at\-scale liquid\-cooled deployments
Develop and validate mechanical structures including chassis and enclosures for manufacturability, reliability, and serviceability. Owning tolerance stack\-up analysis, GD\&T, and DFM for high\-volume manufacturing methods (stamping, bending, extrusion, die\-casting)
Own mechanical design of integrating high\-speed interconnect subsystems including cable cartridges, backplane connectors, and mating interfaces — defining alignment, gatherability, insertion force, and serviceability requirements
Perform structural FEA for shock, vibration, and transportation loads to ensure mechanical integrity across the product lifecycle
Fleet Operations \& Reliability
Participate in on\-call rotations monitoring fleet thermal telemetry for emergent issues
Perform root cause analysis of thermal and mechanical failures in production, implementing firmware updates, hardware modifications, or operational procedure changes
Cross\-Functional Leadership
Drive design standardization
Engage with ODMs and component suppliers to drive design optimization, cost reduction, and supply chain resilience
Lead design reviews, mentor junior engineers, and contribute to the technical direction of the broader organization
Influence chip packaging decisions and establish validation methodologies
About the team
In 2015, Annapurna Labs was acquired by Amazon Web Services (AWS). Since then, we have developed products that power every layer of the AWS cloud, including AWS Nitro, Graviton processors, and custom ML/AI accelerators — Trainium for training and Inferentia for inference — that enable customers to build and run generative AI applications at scale.
The ML/AI MTE team is part of the Annapurna ML/AI hardware development organization. We design and deliver the thermal and mechanical systems for every generation of custom ML/AI accelerator hardware — from chip package through rack\-level infrastructure. Our platforms operate at massive scale across AWS data centers globally, with current programs including next\-generation Trainium and Inferentia systems featuring liquid cooling at scale.
BASIC QUALIFICATIONS
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- BS degree in mechanical engineering or equivalent
- 7\+ years industry experience in Mechanical and Thermal design in electronics packaging
- Experience in thermal and performance measurements and characterization on SoCs, Servers, or related Systems
- 3\+ years of experience in SoC Thermal modelling and IC package transient thermal response
- Experience with chip package, system mechanical \& thermal design for air\-cooled and liquid\-cooled systems
- Experience with tolerance analysis (stack\-up/GD\&T) and sheet metal or precision\-machined component design for electronics packaging
- Experience with production fleet support: telemetry analysis, root cause analysis, and defining mitigation strategies
- Experience leading cross\-functional technical projects spanning silicon, firmware, mechanical, and operations teams
PREFERRED QUALIFICATIONS
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- Knowledge of generic mechanical room infrastructure such as chillers, cooler units, and fan controls
- Experience in mentoring, leading, or managing more junior engineers
- Knowledge of SoC thermal/mechanical design methodology, power modeling and thermal analysis techniques
- Tool familiarity: Ansys Icepak, FloTherm, Cadence Celsius, PTC Creo, and Solidworks
- Proficiency in 3D CAD modeling and engineering drawings per ASME Y14\.5 (GD\&T) standards
- Programming experience: Bash script, Shell script, Linux, Python, and Lua. Familiarity with working in a Linux environment
- Knowledge of various types of technologies used for heatsink solutions, Thermal Interface Materials (TIMs), and liquid cooling technologies
- Knowledge of hardware and software based thermal/power management control algorithms
- Experience with liquid cooling system design: rack manifolds, cold plates, quick\-disconnect fittings, CDU integration
- Experience with mechanical design of high\-density interconnect systems (cable cartridges, blind\-mate connectors, backplane assemblies)
- Experience with high\-power\-density ML/AI accelerator or HPC thermal design
- Track record of driving cost optimization through vendor strategy, second\-sourcing, or design\-for\-manufacturing improvements
- Familiarity with manufacturing processes: injection molding, sheet metal fabrication, die\-casting, and CNC machining — including trade\-offs between methods
- Experience with shock, vibration, and structural qualification testing per ISTA, ASTM, or equivalent standards
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.
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, CA, Cupertino \- 183,000\.00 \- 247,600\.00 USD annually
USA, TX, Austin \- 159,200\.00 \- 215,300\.00 USD annually
USA, WA, Seattle \- 159,200\.00 \- 215,300\.00 USD annually
Salary Context
This $159K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amazon Web Services, 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 Required
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($187K) sits 13% below the category median. Disclosed range: $159K to $215K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Amazon Web Services AI Hiring
Amazon Web Services has 93 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span New York, NY, US, Arlington, VA, US, Cupertino, CA, US. Compensation range: $160K - $350K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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