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Salary
$180k – $420k per year
Location
In office (San Francisco)
Seniority
Senior · 5+ years exp
Visa
H-1B filings in 12 months: 3 · for this role: 2
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Oct 31, 2026. Tutor Intelligence scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Tutor Intelligence is an industrial AI and robotics software company that builds AI robotics systems and deploys them in facilities. It was spun out of the MIT Computer Science and Artificial Intelligence Laboratory in 2021 and hires computer vision, electrical, mechanical and test engineers. The company is headquartered in Watertown, Massachusetts, United States.

About Tutor

We believe robots are the biggest lever we have to making the physical world a better, more affordable place. But robots are a rounding error in our society: nearly nine out of ten American factories and warehouses don’t even have a single robot, and robots have yet to make their way into our homes or places of business.

Tutor Intelligence is changing that.

Tutor is a robotics research and deployment company, building generally capable robot hardware and artificial intelligence to enable robots to go where no robots have gone before. Sonny is a semi-humanoid robot with frontier dexterity approaching the human form. Cassie does back-breaking work, moving boxes, pallets, and bulk goods for businesses from family-owned manufacturers to some of the largest brands in the world. Tutor robots are manufactured at scale and operate as a network, combining the whole fleet’s learned experience to train ever more capable robot artificial intelligence.

Founded at MIT and headquartered in Watertown, MA, our team is full stack: we design and manufacture robot hardware, train our own models, work directly with the smallest and largest end-customers, and maintain our fleet across the United States. We’re a team of researchers, engineers, technicians, operators, and sellers who love building things and solving hard problems.

Our mission is grand: robots everywhere, for the common good, raising the standard of living across North America, and then the world.

The Role

Tutor's research team pushes the frontier of robot capability across the full stack. Researchers have access to 100+ robots for data collection and evaluation, a massive network of teleoperators, scaled hardware and manufacturing infrastructure, and the ability to collect real world data at enormous scale. Our work covers the complete lifecycle of real-world data generation and model training from initial behavior cloning to post-training with supervised fine-tuning and reinforcement learning. And researchers and research engineers work closely with applied engineers to develop novel techniques into robot products that can be deployed at scale.

As a research scientist, you'll set the direction for how our robots learn.

What you'll work on

  • Set research directions in robot learning for dexterous manipulation, from framing the problem to results that hold up on robots.
  • Develop methods that learn from large-scale human supervision: teleoperated demonstrations, interventions and corrections, and human feedback used as reward.
  • Design experiments across the DF1 fleet, and build the evaluations that separate a real improvement from noise.
  • Train and post-train vision-language-action models with behavior cloning, supervised fine-tuning, and reinforcement learning.
  • Work with research and applied engineers to turn new techniques into models that run on deployed Cassie and Sonny robots.
  • Share results through internal write-ups and technical reports.

Requirements

  • 5+ years of research experience in robot learning, machine learning, or a related field, through a PhD or equivalent industry experience.
  • A track record of original work in robot learning, imitation learning, reinforcement learning, or large-model training, shown through publications, open-source work, or shipped systems.
  • Strong Python and hands-on experience with PyTorch or JAX.
  • Experience running experiments on physical robots, not only in simulation.
  • Clear communication of results, including the negative ones.

Nice to haves

  • Vision-language-action models, or diffusion and flow-matching policies.
  • Learning from human feedback, interventions, or reward modeling.
  • Bimanual or dexterous manipulation.
  • Large-scale distributed training.
  • Not afraid to step foot in a factory or a warehouse.
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