Founding Research Scientist

MorphLabs · New York City · Full time, on-site

MorphLabs is building non-invasive telekinesis. Our mission is to build the final frontier of control: a brain interface that turns movement intent into action, starting with people underserved by existing technology.

Our vision is a future where, as AI progresses, our ability to move and control our own bodies no longer limits what we can do. We are starting with continuous, intuitive prosthetic control for upper-limb amputees: decoding movement intent in real time, without surgery or reliance on muscle sensors. We develop the hardware and models together, treating signal quality, learning, and control as one problem.

We believe task-specific data, better algorithms, and better hardware are converging to make this possible. Prosthetics are where we start, not where we stop. As the technology improves and new needs become clear, we will expand into applications that give more people control through a brain interface.

We are a small, scrappy, high-throughput team based in NYC. We have raised a substantial pre-seed round, with our seed round underway. We move quickly between research and building, with a clear purpose: put this technology to work for the people who need it most.

The central problem at MorphLabs is learning a mapping from EEG to intended movement that holds up across people and over time. The models that learn this mapping sit at the center of what we build. They drive prosthetic and robotic hands, on-screen cursors, and more.

You'll be our founding research scientist, working directly with our CTO. You'll own your research from the first question to the result, and your findings will shape where our decoding work goes next.

You'll own the research behind our decoders, from new ideas through to models running live in our prosthetic system. You'll report to our CTO and work closely with them to build the models that control our system.

• →Transfer learning across subjects, sessions and devices. Build decoders that adapt to new users from minimal calibration data, stay robust to non-stationarity and drift between sessions, and transfer between headsets.
• →Self-supervised pretraining. Develop pretraining objectives and representations on our growing, largely unlabeled EEG corpus, study how performance scales with data and model size, and measure downstream transfer to real control.
• →Online closed-loop decoding. Move from offline benchmarks to causal, low-latency decoders that adapt online during use, and study why offline accuracy doesn't predict online performance, including user-decoder co-adaptation.
• →Paradigm design. Design the tasks and data-collection protocols that produce informative training data, and choose each next experiment based on what the last one showed.
• →Evaluation and benchmarking. Define the metrics, held-out splits and benchmarks MorphLabs uses to judge decoders, offline and online.
• →Inform the hardware. Use what the models reveal to guide hardware design decisions, set signal-quality standards with our hardware team, and help validate our custom headset against third-party systems.
• →Ship what works. Turn promising research into reproducible decoders running in our system, and write up what worked and what didn't.

• Data, every day. We record new EEG every day, so our corpus is always growing. It already spans thousands of hours. We run experiments on it constantly, and getting more out of it is part of the job.
• Our own hardware. We build our own headset, electrodes and amplifier, and also model data from third-party headsets. You'll help validate our hardware and shape what comes next.
• An IRB already in place, covering both healthy participants and patients.
• The resources to do the work. Compute and equipment won't be the bottleneck.

This is a senior individual contributor role. What you've done matters more than your title, years of experience or degrees. You don't need a PhD or a background in neuroscience or BCI. The best researchers can pick up the domain.

• Research you can point to, such as papers, reports or code, and a clear account of what you did yourself
• Models you built and trained yourself on noisy time series (speech, audio, sensors, robotics and neural data all count), and can explain your architecture and objective choices and how you debugged training and generalization
• Models that kept working when the data shifted, across people, sessions, devices or environments, and an understanding of why
• Rigorous, reproducible work: fair baselines, ablation studies, test sets that hold out whole sessions or people, and results others can reproduce
• Math and statistics you actually use in modeling decisions: probability, optimization, linear algebra and uncertainty
• An experiment or task you designed that produced useful data, or human-subjects research, including under IRB-approved protocols
• The ability to run experiments quickly and let each result sharpen the next one, while working independently and writing clearly

• A background in dynamical systems, geometry or information theory that shapes how you think about representations, time structure or model design
• Experience with biosignals, neural data or non-invasive BCIs
• Experience with real-time or closed-loop systems, where latency matters and offline accuracy isn't the whole story
• Experience learning from limited data: self-supervision, and choosing the next experiment when data is scarce
• Pretraining experience where the results carried over to a downstream task
• First-author papers or other substantial research results. Quality matters more than venue.

These are guidelines, not a checklist. Exceptional researchers often come from unconventional backgrounds, so apply even if you're not an exact fit.

1. Quick screen
2. Technical interview
3. Culture fit call with the full founding team
4. Chat with references
5. 1 week work trial
6. Offer

Target start date: December 2026, with flexibility for the right candidate.