Machine Learning Engineer at Neuralink
- Pay: $129,000–$331,000 a year.
- Full-time.
- In Austin, TX.
About Neuralink: We are creating devices that enable a bi-directional interface with the brain. These devices allow us to restore movement to the paralyzed, restore sight to the blind, and revolutionize how humans interact with their digital world. The opportunity In January 2024, a man paralyzed below the shoulders received a Neuralink implant. Within days he was playing chess and Civilization VI by imagining a cursor moving. That was our first product experience: computer control decoded from 1,024 electrodes in the brain’s motor cortex. Since then, 20+ people have been using the device, some for 17+ hours per day. We are now working on two problems nobody has solved. Decoding the full virtual arm. This requires deciphering 29 degrees of freedom against millisecond timescale neural spikes. The published state of the art is four degrees of individuated finger control (Willsey et al., Nature Medicine 2025). We aim to fully decipher human intent of controlling anything that a human hand is capable of doing, and build a product that our users rely on for their independence every day. Brain to voice. The frontier of real-time voice synthesis from intracortical signals is intelligible roughly half the time and carries only coarse pitch control (Wairagkar et al., Nature, 2025). We are working towards prosodic speech, in users’ own voices, streaming fast enough to hold a natural conversation. If you want to help us solve these problems, we want to hear from you. Why this is an interesting machine learning problem Data. Train on >50,000 hours of neural data from clinical trial participants Nonstationarity. Tackle the tough open problem that neural activity drifts and we need to solve decoding while minimizing user recalibration Co-adaptation. The brain adapts to your decoder while your decoder adapts to the brain Strict constraints. The brain implant operates on a tight power and the participant experience requires optimizing every millisecond of latency Your eval is a person. Success is a human being capable of doing something today that they couldn't do yesterday What you'll work on Train models on neural spike data across sessions and participants so a new user’s decode works out of the box, and stays working through weeks of drift without supervised recalibration Design online adaptation that keeps the closed loop stable while both the model and the user are learning Compress a full hand-and-arm decoder to run with a tight latency budget on the user’s device Build the pre-training and post-training stack for voice synthesis: self-supervised representation learning on neural recordings, supervised fine-tuning on paired data, reinforcement learning with intelligibility and naturalness rewards, feeding a streaming vocoder that produces speech in real time Define what evidence justifies shipping a model update to someone’s brain-computer interface, then build the eval and rollout infrastructure to meet that bar Feed learnings back into the hardware platform. …