Description review
Research Engineer, Human Biomechanics
Relari · San Francisco, California, US · back to the listing
HR standards
65/100
needs work
Title ↔ description
74/100
solid
Reads as
Machine Learning Engineer
85% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Applied AI Engineer Automattic
- Machine Learning Engineer, CX Intelligence Coinbase
- AI Engineer - FDE (Forward Deployed Engineer) Databricks
- AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
- Senior AI Engineer – Notebooks Datadog
Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
- Member of Technical Staff (applied) Anthrogen
- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
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.