Interested in this Research Scientist role at Amazon.com?
Apply Now →Skills & Technologies
About This Role
DESCRIPTION
---------------
Amazon is seeking a world\-class Sr. Applied Scientist to lead the development of next\-generation object tracking systems for autonomous robots operating at Amazon's scale. In this role, you will architect robust, real\-time tracking pipelines that fuse information across multiple sensor modalities — combining the rigor of classical estimation theory with the power of modern learning\-based approaches to deliver tracking systems that are accurate, reliable, and scalable in complex, dynamic environments.
Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting\-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real\-world environments.
We leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started.
As a Sr. Applied Scientist working on Tracking and Sensor Fusion, you will own the design and delivery of tracking systems that enable robots to maintain persistent, accurate awareness of objects, humans, and dynamic elements in their environment. You will bring deep expertise in multi\-sensor fusion, Bayesian estimation, and Kalman filtering — paired with a strong command of modern learning\-based tracking methods — to build systems that are both principled and adaptive.
Your work will be foundational to safe and intelligent robot behavior: enabling downstream planning, navigation, and manipulation systems to operate with confidence in the presence of uncertainty and change. You will lead research that bridges classical state estimation with data\-driven approaches, collaborating with world\-class teams pushing the boundaries of robotic perception, autonomy, and human\-robot interaction.
Join us in building intelligent tracking and fusion systems that will define the future of autonomous robotics at scale.
Key job responsibilities
- Lead the research, design, and development of multi\-object tracking (MOT) systems for autonomous robots, combining classical and learning\-based approaches
- Develop and deploy sensor fusion pipelines that integrate data from cameras, depth sensors, radar, IMUs, and other sensor modalities using principled estimation frameworks (Extended Kalman Filters, Unscented Kalman Filters, Particle Filters, factor graphs)
- Pioneer learning\-based tracking methods including neural data association, learned motion models, transformer\-based trackers, and end\-to\-end differentiable tracking architectures
- Design robust track management systems — including track initialization, association, occlusion handling, re\-identification, and track lifecycle management
- Develop and validate tracking systems that operate reliably in real\-time under challenging conditions: occlusion, clutter, sensor noise, and dynamic scene changes
- Collaborate closely with Perception, Navigation, Planning, and Controls teams to deliver integrated autonomy solutions
- Establish benchmarks, evaluation frameworks, and safety validation protocols for tracking systems
- Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high\-impact delivery
- Publish research findings in top\-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents
A day in the life
- Train ML models for deployment in simulation and real\-world robots, identify and document their limitations post\-deployment
- Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations
- Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team
- Mentor team members while maintaining significant hands\-on contribution to technical solutions
About the team
Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.
BASIC QUALIFICATIONS
------------------------
- PhD in computer science, electrical engineering, or related field
- 5\+ years of experience in object tracking, sensor fusion, or state estimation for autonomous systems
- Deep expertise in Bayesian estimation and filtering — including Kalman Filters (EKF, UKF), Particle Filters, and multi\-hypothesis tracking
- Strong experience with multi\-sensor fusion across heterogeneous sensor suites (camera, LiDAR, radar, IMU)
- Demonstrated experience with learning\-based tracking approaches (e.g., neural association, graph neural networks for tracking, attention\-based trackers, learned motion prediction)
- Proficiency in Python and/or C\+\+; experience with deep learning frameworks (PyTorch, TensorFlow, or equivalent)
- Proven track record of delivering tracking/fusion systems to production
- Strong publication record in top\-tier computer vision, robotics, or ML venues
PREFERRED QUALIFICATIONS
----------------------------
- Experience in the autonomous driving industry, particularly in developing and deploying real\-time object tracking and sensor fusion pipelines for self\-driving vehicles
- Hands\-on experience building production\-grade 3D multi\-object tracking systems
- Experience with foundation models or large pre\-trained representations applied to tracking or temporal reasoning
- Knowledge of graph\-based optimization, factor graphs (GTSAM, Ceres), or probabilistic graphical models
- Experience with sim\-to\-real transfer and synthetic data generation for tracker training and evaluation
- Familiarity with tracking benchmarks and datasets (nuScenes, KITTI, Waymo Open Dataset, MOT Challenge, Argoverse)
- Experience with real\-time systems, latency\-constrained inference, and edge deployment
- Knowledge of SLAM, localization, or mapping systems and their interaction with tracking
- Experience with ROS/ROS2 and real\-time robotics middleware
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.
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, SAN FRANCISCO \- 192,200\.00 \- 260,000\.00 USD annually
Salary Context
This $192K-$260K range is above the 75th percentile for Research Scientist roles in our dataset (median: $183K across 109 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,823 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 280 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $192K to $260K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 102 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Product Manager, Data Scientist. Positions span New York, NY, US, Palo Alto, CA, US, Bellevue, WA, US. Compensation range: $129K - $300K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.