Neural Decoding ML Engineer - Deployable Brain-Signal Models

NeuroDoctrine; Los Angeles (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

NeuroDoctrine in Los Angeles is seeking a machine-learning engineer to develop neural-decoding models that translate brain signals into commands for swarm and C2 systems, from dataset to on-device inference.

You will collaborate with signal-processing and neuromorphic engineers, evaluate models under noisy real-world data, and ensure deployment robustness in field conditions.

Strong experience in time-series ML with PyTorch or JAX is required.

Qualifications

• Experience with ML for time-series or biosignals.
• Ability to evaluate models under noisy real-world data.
• Bias toward deployable, field-ready systems.

Responsibilities

• Own decoding models from dataset to on-device inference.
• Collaborate with signal-processing and neuromorphic engineers.
• Keep models honest against field conditions.

Skills

Time-series ML

Biosignals

Noisy data evaluation

Tools

PyTorch

JAX

Job description

NeuroDoctrine in Los Angeles is seeking a machine-learning engineer to develop neural-decoding models that translate brain signals into commands for swarm and C2 systems, from dataset to on-device inference.

You will collaborate with signal-processing and neuromorphic engineers, evaluate models under noisy real-world data, and ensure deployment robustness in field conditions.

Strong experience in time-series ML with PyTorch or JAX is required.