sEMG / Biosignal ML Scientist

About REVEL

REVEL is an AI robotics company developing physical intelligence for general-purpose humanoid robots. We capture the force, dexterity and intent of human work with our Neural Gambit wearable, and use it to train RAI, the intelligence that powers our robots. REVEL is headquartered in Palo Alto, California, with R&D and engineering facilities in Prague and Hradec Králové, Czech Republic. This role is on-site with our engineering team in the Czech Republic.

The intent layer is REVEL's core differentiator: reading neuromuscular signals 30-100 ms before motion. We're looking for a scientist to decode high-density surface EMG into force and intent.

The Role

We are looking for a scientist to develop methods for decoding high-density surface EMG (HD-sEMG) into continuous measures of human movement, force and motor intent.

A central research question is how much information about forthcoming movement and mechanical output can be extracted from neuromuscular activity before observable movement occurs. You will investigate this temporal relationship experimentally and develop models that can exploit it under realistic conditions.

This is a highly experimental role. You will work with real human data, noisy physiological signals, wearable hardware and evolving data-collection protocols, while translating scientific findings into models that can ultimately support robotic intelligence.

Responsibilities

• Develop signal-processing and machine-learning methods for decoding HD-sEMG into continuous force, joint kinematics and motor intent.

• Characterise the temporal relationship between neuromuscular activity and mechanical movement, including the potential 30–100 ms anticipatory window.

• Design experiments to quantify the reliability, information content and limits of anticipatory information in sEMG.

• Develop preprocessing and signal-quality methods for multi-channel EMG, including filtering, artefact handling, normalization and channel-quality assessment.

• Investigate the effects of sensor placement, electrode contact, motion artefacts, fatigue, physiological variability and session-to-session drift.

• Evaluate model robustness across operators/subjects, sessions, movements and sensor configurations.

• Develop methods for cross-operator, cross-session and cross-condition generalisation, including efficient calibration where necessary.

• Design capture and labelling protocols together with the data-collection team.

• Define data-quality criteria and experimental metrics that enable reliable downstream ML.

• Collaborate closely with the robotics ML team to translate biosignal representations and decoded intent into inputs suitable for RAI.

• Prototype and evaluate models in real-time or near-real-time settings where appropriate.

• Communicate experimental results clearly and use them to guide the next iteration of the sensing, modelling and data-collection system.

Requirements

• Hands-on experience with EMG or electrophysiological biosignals, including signal acquisition, preprocessing and quantitative analysis.

• Strong understanding of signal processing for noisy, non-stationary physiological signals.

• Experience developing machine-learning models for time-series or sequential data.

• Strong Python skills and practical experience with PyTorch or JAX.

• Experience designing, executing and analysing controlled experiments.

• Ability to reason quantitatively about temporal alignment, latency, prediction, signal quality and model generalisation.

• Experience working with real-world biological data where signal characteristics vary across people and sessions.

• Strong scientific judgement and the ability to distinguish genuine physiological information from artefacts, leakage and experimental confounds.

Bonus qualifications

• PhD or equivalent research experience in biomedical engineering, neuroscience, electrical engineering, biomechanics, computer science, machine learning or a related field; alternatively, an MSc with 2–3+ years of highly relevant research or industry experience.

• Experience with high-density sEMG / electrode arrays.

• Experience with multi-channel EMG decomposition, motor-unit activity or neuromuscular physiology.

• Experience decoding force, torque, joint kinematics or motor intention from biosignals.

• Background in motor control, biomechanics or human movement science.

• Experience with wearable sensing and human-subject experiments.

• Experience with domain adaptation, calibration or cross-subject generalisation.

• Familiarity with real-time inference and latency constraints.

• Experience deploying ML models outside purely offline research environments.

Bonuses

• Work That Ships: We capture how skilled humans work, their force, touch and judgment, and our robots do the work. You put robots on a paying customer's floor, not in a demo loop

• The Team: Colleagues from NVIDIA, SpaceX and Neura Robotics, and founders you work with directly. No layers, no process between you and the decisions

• Equity for Key Roles: For select positions, meaningful stock options mean you're not just working here, you own a piece of the outcome

• Salary and Quarterly Bonus: Strong base pay plus a quarterly bonus, in a city where it goes further

• Unlimited Paid Time Off: Real flexibility to take time away when you need it. We trust our people to own their work, their time and their results

• Your Own Hardware: A top-spec GPU workstation, cluster access, and hands-on time with the robots you're building. Not a software sandbox

• Keep Learning: Conference budget for select roles (GTC, CoRL, ICRA), and room to publish and contribute to open source where our IP allows

• Prague, On-Site: Robots need hands, so we work together in our Prague lab. Moving here? We sponsor your work visa and cover relocation

• Health and Fitness: Multisport card (from December 2026), extra paid sick days, and an employer pension contribution

• Lunch, On Us: Complimentary lunch every working day, plus coffee, snacks and drinks whenever you need a boost

How to apply

• Apply through the link on this posting. Include a short description of an environment you administered or took over: its size, what you changed about how access or devices were managed, and what you would do differently now.