Research Scientist, Dexterous Manipulation & Robot Learning

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

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Salary

$176K-$304K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

106% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Research Scientist, Dexterous Manipulation & Robot Learning Cambridge, MA USA Your Impact at LILA As a Robotics Scientist at Lila, you will lead the research and development of autonomous robotic systems that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You'll develop novel algorithms and deploy intelligent robotic solutions that interact seamlessly with human scientists and complex lab environments. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering. What You'll Be Building - Pioneering approaches for precise and dexterous robotic manipulation that leverage foundation models, reinforcement learning, diffusion-based methods, and human guidance to enable adaptive and intelligent robotic systems capable of complex tasks across diverse scientific environments - Developing novel human-robot interaction frameworks that incorporate imitation learning, and learning from human guidance, feedback, demonstrations and corrections, creating intelligent robotic agents that can seamlessly integrate with human scientific workflows and rapidly adapt to new experimental contexts - Advancing dexterous manipulation research through cutting-edge machine learning approaches, including diffusion models and adaptive learning algorithms, that synthesize multi-modal sensing (tactile, visual, and language) to develop generative skill representation sand sophisticated motor learning policies for intelligent robotic systems - Designing autonomous robotic systems with trust calibration mechanisms, enabling intelligent agents that can dynamically adjust their behaviors based on contextual information in complex scientific tasks What You'll Need to Succeed - Ph.D. in Robotics, Machine Learning, Computer Science, or a related field with demonstrated expertise in foundation models for

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