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DESCRIPTION
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We are seeking a Member of Technical Staff \- Mechanical Engineer to design and develop robotic manipulation hardware within a frontier AI and robotics research lab. You will own the mechanical design of manipulation\-focused hardware end\-to\-end: grippers, end\-of\-arm tooling (EOAT), multi\-finger hands, UMI\-style grippers, and data collection fixtures \- all built to enable AI researchers to collect high\-quality demonstration data and validate learned manipulation policies.
This role sits at the intersection of mechanical design and AI research. You will be the person who builds the physical tools that make robot learning possible, rapidly prototyping and iterating on end\-effectors and data collection hardware, working hand\-in\-hand with AI/ML researchers to understand what mechanical properties (compliance, sensing, geometry) actually matter for learned manipulation policies.
The ideal candidate is hands\-on, moves quickly, is motivated by learning across domains, and is as comfortable sketching a new gripper concept as they are debugging a real\-world manipulation experiment. You thrive in fast\-paced R\&D environments where requirements evolve quickly, and you are genuinely curious about how your design decisions influence data quality and policy performance. If you want to directly shape the hardware that enables the next generation of robot learning, this role is for you.
What You Bring:
- A hands\-on, build\-first mindset: comfortable prototyping, testing, and iterating rapidly in a research environment.
- Comfort designing in environments where requirements evolve quickly and hardware needs to be rethought from week to week
- Genuine interest in how mechanical design choices such as compliance, geometry, sensing integration, etc. impact data quality for robot learning
- Familiarity or curiosity about imitation learning, teleoperation, or data collection for manipulation
- A collaborative and communicative working style, especially in multi\-disciplinary research environments spanning AI, controls, and perception
- A passion for robotics and advancing the state of the art in dexterous, capable manipulation systems
Key job responsibilities
- Design grippers, end\-of\-arm tools, and low DoF multi\-finger hands for robotic manipulation research and data collection.
- Develop UMI\-style grippers and teleoperation hardware for collecting manipulation demonstration data.
- Design fixtures, jigs, and mounting systems for cameras, sensors, and manipulation test setups.
- Integrate tactile sensors, force/torque sensors, and cameras into compact gripper assemblies.
- Rapidly prototype and iterate on manipulation hardware — from concept sketches to functional grippers in days/weeks.
- Partner with AI researchers to understand what mechanical properties (compliance, sensing, geometry) matter for learned manipulation policies.
- Design data collection devices, such as UMI grippers, that enable repeatable, high\-quality demonstration capture.
- Conduct mechanical testing of gripper performance (grasp force, compliance, durability, repeatability).
- Apply DFM/DFA to scale successful gripper designs from one\-offs to small batches (10s–100s of units).
- Own cable routing, actuation, and sensing integration for compact end\-effector designs.
- Support hands\-on builds, debug sessions, and real\-world manipulation experiments.
- Collaborate with controls, perception, and AI teams to ensure hardware meets research needs.
About the team
Frontier AI \& Robotics (FAR) is the team at Amazon building the next generation of embodied intelligence. FAR drives the development and implementation of advanced AI models within Amazon’s operations that enable robots to see, reason, and act on the world around them, supporting a number of different warehouse automation tasks.
BASIC QUALIFICATIONS
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- Bachelor's degree in mechanical engineering, mechatronics engineering, or equivalent
- 3\-5\+ years designing mechanical systems for robotics, grippers, end\-effectors, or complex electromechanical products
- Proficiency in 3D CAD (SolidWorks, OnShape, or equivalent)
- Hands\-on experience building prototypes, integrating hardware, and troubleshooting mechanical issues
- Experience designing compact mechanisms with integrated actuation and sensing
- Experience with rapid prototyping methods (3D printing, CNC, laser cutting, sheet metal)
- Familiarity with actuator/motor selection for compact, force\-controlled applications
- Experience with structural analysis and material selection for lightweight, functional parts
PREFERRED QUALIFICATIONS
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- Master's degree in mechanical engineering, mechatronics, or robotics
- Experience designing robotic grippers, end\-of\-arm tools, or dexterous manipulation hardware
- Familiarity with tactile sensing, force/torque sensing, or compliant mechanism design
- Experience with teleoperation hardware or data collection systems for robot learning
- Exposure to imitation learning, learning from demonstrations, or sim\-to\-real transfer concepts
- Experience with cable\-driven mechanisms, tendon routing, or underactuated hand designs
- Knowledge of DFM/DFA principles for small\-batch production
- Experience working in research environments with rapidly evolving requirements
- Basic familiarity with ROS, controls, or embedded systems for integrated design work
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.
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.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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.com AI Hiring
Amazon.com has 122 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, AI Software Engineer. Positions span Seattle, WA, US, Santa Clara, CA, US, New York, NY, US. Compensation range: $128K - $338K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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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