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Senior Machine Learning Researcher Jobs (NOW HIRING)

Inference

Machine Learning Researcher

San Francisco, CA · On-site

$250K - $350K/yr

Perform reinforcement learning research to improve model alignment and capability * Develop and improve our distillation pipeline for training high-quality models from frontier teachers * Train ...

PROPRIUS

Senior Machine Learning Engineer

New York, NY

$114.30K - $157K/yr

Sr. Machine Learning Engineer Location: New York, NY Sponsorship: Yes Relocation: Yes Industry ... When successful, your research will be deployed into millions of vehicles worldwide. This is an ...

Expedia

Senior Machine Learning Scientist

Seattle, WA · On-site +1

$104.10K - $142.30K/yr

Senior Machine Learning Scientist The Senior Machine Learning Scientist is responsible for building ... Read and apply relevant academic and industry research to improve model architectures, training ...

Expedia

Senior Machine Learning Scientist

San Jose, CA · On-site +1

$107.20K - $146.50K/yr

Senior Machine Learning Scientist The Senior Machine Learning Scientist is responsible for building ... Read and apply relevant academic and industry research to improve model architectures, training ...

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How much do senior machine learning researcher jobs pay per year?

As of May 29, 2026, the average yearly pay for senior machine learning researcher in the United States is $76,607.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $98,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Researcher, and why are they important?

To thrive as a Senior Machine Learning Researcher, you need advanced knowledge in machine learning algorithms, statistical analysis, programming (typically in Python), and a relevant advanced degree such as a PhD or Master's in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, as well as familiarity with cloud computing platforms and research publication, is often required. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and present complex ideas clearly. These skills and qualities are essential for driving innovation, developing robust models, and translating research into practical, impactful solutions.

What opportunities for collaboration typically exist for Senior Machine Learning Researchers within a company?

Senior Machine Learning Researchers frequently collaborate with cross-functional teams, including data engineers, software developers, and domain experts. This collaboration ensures that research insights are effectively translated into scalable solutions and integrated into products or services. Researchers often participate in brainstorming sessions, code reviews, and joint publications, fostering a culture of innovation and shared knowledge. These interactions not only drive the success of projects but also provide valuable learning experiences and networking opportunities.

What does a Senior Machine Learning Researcher do?

A Senior Machine Learning Researcher leads the development and application of advanced machine learning models to solve complex problems. They are responsible for designing experiments, analyzing large datasets, publishing research findings, and collaborating with engineering teams to implement solutions. Additionally, they mentor junior researchers, stay updated with the latest advancements in AI, and often contribute to setting the research agenda for their organization.

What is the difference between Senior Machine Learning Researcher vs Data Scientist?

AspectSenior Machine Learning ResearcherData Scientist
CredentialsAdvanced degrees in CS, ML, or related fieldsDegree in CS, statistics, or related fields; certifications optional
Work EnvironmentResearch labs, R&D teams, academiaBusiness analytics, product teams, startups
Industry UsageResearch-focused roles in tech, academia, R&DData analysis, business insights, product development
Search & Comparison IntentUnderstanding research vs applied roles in MLExploring data analysis careers and skills

While both roles involve working with data and machine learning, a Senior Machine Learning Researcher primarily focuses on developing new algorithms and advancing ML theory in research settings. In contrast, a Data Scientist applies existing models to analyze data, generate insights, and support business decisions. The roles differ mainly in their focus—research innovation versus practical application—though they share overlapping skills and credentials.

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Senior Machine Learning Researcher jobs near you
Senior Machine Learning Researcher, Large Behavior Models & Diffusion Policy

Senior Machine Learning Researcher, Large Behavior Models & Diffusion Policy

Toyota Research Institute

Los Altos, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 9 days ago

Job description

At Toyota Research Institute (TRI), we're on a mission to improve the quality of human life. We're developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we've built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.


The Team
 
The Automated Driving Advance Development division at TRI focuses on enabling innovation and transformation at Toyota by building a bridge between TRI research and Toyota products, services, and needs. We achieve this through partnership, collaboration, and shared commitment. The Automated Driving Advance Development team is leading a new cross-organizational project between TRI and Woven by Toyota to research and develop a fully end-to-end learned automated driving / ADAS stack. This cross-org collaborative project is synergistic with TRI's robotics divisions' efforts in Diffusion Policy and Large Behavior Models (LBM).
 
The Opportunity
 
We are looking for a Senior Machine Learning Researcher to join us in developing a state-of-the-art, pixels-to-action, end-to-end system for automated driving. As an expert in machine learning, you will contribute to designing and developing innovative models for our autonomy stack and deploying them on vehicle platforms to solve daily driving tasks and handle long-tail scenarios.
 
An ideal candidate has a strong track record of leading independent research efforts, preferably including mentoring and collaborating with less experienced students and researchers. You will help to drive our exploration into end-to-end learning approaches for automated driving, using large-scale sensor data directly for perception, planning, and prediction to overcome traditional "information bottlenecks." This includes expanding our successful Large Behavior Model (LBM) robotics efforts and Diffusion Policy (DP) research into the driving domain, designing scalable architectures, and integrating visual-language-action modalities. Beyond refining models for closed-loop driving on public roads and in simulation, you will also explore data quality filtering, transfer learning from diverse data sources, and edge deployment optimization. This work is part of Toyota's global AI efforts to build a more coordinated global approach across Toyota entities.
Responsibilities
  • Conduct ambitious research to advance the state-of-the-art in using new capabilities in generative AI (e.g., recent results in diffusion policy [1],[2]) for end-to-end perception, planning, and prediction in automated driving with a focus on computer vision as the primary sensing modality.
  • Research and implement scalable end-to-end architectures that process raw sensor data to generate vehicle trajectories, addressing the challenges of long-tail driving scenarios with low data coverage.
  • Prototype, validate, and iterate model architectures using imitation learning and large-scale data, ensuring robust performance across diverse scenarios.
  • Perform closed-loop evaluations in sensor simulations and real-world testing environments to rigorously assess model performance, stability, and scalability.
  • Explore multi-modal and language-conditioned models to broaden the applicability of end-to-end policies, using external data sources and transfer learning to enhance generalization.
  • Collaborate with researchers and engineers across TRI, Woven by Toyota, and Toyota's global ecosystem to accelerate model deployment and evaluation in both controlled environments (closed-course) and public road driving.
  • Take the lead on writing and publishing research results in peer-reviewed venues.
Qualifications
  • A PhD or equivalent experience in a robotics-relevant or embodied-AI field such as Computer Science, Mathematics, Physics, or Engineering.
  • A consistent track record of publishing at high-impact conferences/journals (CVPR, ICLR, NeurIPS, ICML, CoRL, RSS, ICRA, ICCV, ECCV, PAMI, IJCV, etc.)
  • A consistent track record of independent research.
  • Demonstrated ability to independently formulate and complete a research agenda while collaborating across subject areas.
  • Experience training large-scale models, including foundation models (e.g., vision-language models, text-to-video models).
  • Proficiency in Python and C++ for implementing and evaluating research ideas.
Bonus Qualifications
  • Experience with robot motion planning techniques like trajectory optimization, sampling-based planning, and model predictive control, or experience with automated driving domains (e.g., perception, prediction, mapping, localization, planning, simulation).
  • Experience in developing production-level code for real-time operating systems.
  • Experience optimizing runtime-critical systems for Linux, UNIX-like real-time operating systems on automotive-grade compute platforms, and building safety-critical software architectures.
Please add a link to Google Scholar and include a full list of publications when submitting your CV for this position.
 
The pay range for this position at commencement of employment is expected to be between $200,000 and $287,500/year for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.
 
TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant's race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.
 
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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