Research Engineer, Human Biomechanics

Relari; San Francisco (CA); On-site; USD 130,000 - 190,000; Full time

Job summary

Relari is seeking a researcher/engineer to advance multimodal representations of human biomechanics for robot learning. You will design experiments spanning EMG, force, tactile sensing, motion, and touch feedback, creating datasets and models that transfer across people and tasks.

Hands-on experience with biomechanical data and strong Python skills are essential. A PhD is welcome but not required if depth through work is demonstrated.

Qualifications

• Experience collecting and analyzing biomechanical or physiological data (EMG, force, tactile sensing, motion, wearables).
• Strong quantitative skills in signal processing, statistics, time-series modeling, sensor fusion, or ML.
• Fluency in Python and building reliable modeling pipelines around imperfect, synchronized sensor data.

Responsibilities

• Develop signal-processing and learning methods for synchronized EMG, force, tactile, pressure, motion, and visual data.
• Design experiments and sensor configurations capturing how humans regulate contact, stability, coordination, and effort during manipulation.
• Build multimodal representations that connect neuromuscular activity with external forces and touch feedback.
• Develop calibration and domain-adaptation methods across people, sessions, tasks, and sensor configurations.
• Create data-collection protocols grounded in biomechanics and downstream robot-learning needs.
• Work with robot-learning researchers to test whether physical-interaction representations improve policy behavior and cross-embodiment transfer.
• Influence wearable sensing, force and tactile hardware, validation procedures, and future measurement systems.

Skills

Python

Biomechanical data analysis

Signal processing

Sensor fusion

Machine learning

Education

PhD welcomed but not required

Fields: biomechanics, neuroengineering, biomedical engineering, motor control, computational neuroscience, haptics, kinesiology

Tools

Python

Job description

About The Role

You will bring a deep understanding of human movement and physical interaction into the same research loop as our robot-learning engineers. You will design experiments and computational representations spanning muscle activity, force, pressure, tactile contact, motion, and touch feedback. The goal is to determine which structure can be measured reliably and transferred across people, tasks, sensors, and robot embodiments. This is an engineering role: ideas should become code, datasets, models, and robot experiments.

About The Role

You will bring a deep understanding of human movement and physical interaction into the same research loop as our robot-learning engineers. You will design experiments and computational representations spanning muscle activity, force, pressure, tactile contact, motion, and touch feedback. The goal is to determine which structure can be measured reliably and transferred across people, tasks, sensors, and robot embodiments. This is an engineering role: ideas should become code, datasets, models, and robot experiments.

What You’ll Do

• Develop signal-processing and learning methods for synchronized EMG, force, tactile, pressure, motion, and visual data.
• Design experiments and sensor configurations that capture how humans regulate contact, stability, coordination, and effort during manipulation.
• Build multimodal representations that connect neuromuscular activity with external forces and touch feedback.
• Develop calibration and domain‑adaptation methods that remain reliable across people, sessions, tasks, and sensor configurations.
• Create data‑collection protocols grounded in biomechanics and downstream robot‑learning needs.
• Work with robot‑learning researchers to test whether physical‑interaction representations improve policy behavior and cross‑embodiment transfer.
• Influence wearable sensing, force and tactile hardware, validation procedures, and future measurement systems.

What We’re Looking For

• Research or engineering depth in biomechanics, neuroengineering, biomedical engineering, motor control, computational neuroscience, haptics, kinesiology, or a related field.
• Hands-on experience collecting and analyzing one or more forms of biomechanical or physiological data, such as EMG, force, pressure, tactile sensing, motion capture, or wearable-sensor data.
• Strong quantitative skills in signal processing, statistics, time‑series modeling, sensor fusion, or machine learning.
• Fluency in Python and the ability to build reliable modeling pipelines around imperfect, synchronized sensor data.
• Interest in connecting human physical interaction with learned robot behavior; a PhD is welcome but not required when equivalent depth is demonstrated through other work.

Working at Relari

Relari is a small research and engineering startup developing new ways for robots to learn dexterous skills from human biomechanics. Our founders have AI research roots at MIT and NVIDIA, along with autonomous‑vehicle and robotics deployment experience at Pony AI and Dexterity. We are backed by top investors including Y Combinator, General Catalyst, and Soma Capital. You will work directly with the founders, own problems end to end, and test your ideas on real robotic systems. This role is full‑time and on‑site in San Francisco.