Principal Software Engineer, Neuroscience Platforms

What You Will Build

• Stimulus presentation and acquisition systems, including VR environments built in Unity, Unreal, or comparable frameworks, that synchronize peripheral streams (eye tracking, displays, haptics) with high-throughput neural recording hardware to millisecond precision.
• High-throughput pipelines to ingest, process, and store massive, multi-modal neural datasets (electrophysiology, calcium imaging, behavior), with versioned data formats, automated QC, and clear metadata standards.
• Dashboards, user interfaces, and experiment-design tools that let scientists interact with complex data and run experiments seamlessly.
• Cloud-based storage, processing and sharing infrastructure that supports distributed teams and large-scale datasets.

What We Are Looking For

• Exceptional software engineering fundamentals, proven in production. You write clean, testable, well-documented code and approach system design thoughtfully.
• The range and independence to take a rough idea or open-ended goal, scope a path to a working system, and move forward without waiting for a complete spec.
• A deep intuition for latency, hardware limits, and data bottlenecks, earned building real-time or performance-critical systems in production (game engines, robotics, embedded systems, audio/video pipelines, or similar).
• Proficiency in at least two of: Python, C++, C#, Rust.
• Intellectually curious, collaborative, and eager to learn the science around you.
• A track record of owning complex systems end-to-end, from architecture and implementation through operational reliability, and of making and defending technical tradeoffs across performance, maintainability, and team velocity.
• Comfort working across the full stack, from low-level hardware interfaces to cloud infrastructure, with the ability to build and iterate on human-facing research systems with speed and taste.
• A history of mentoring engineers and leading technical initiatives, with the judgment to set technical direction across teams.

Bonus

• Familiarity with brain-machine interfaces, neural data acquisition systems, neural data formats and analysis tools.
• Background in machine learning, particularly online/streaming inference or neural decoding.
• Experience designing data pipelines at scale (Airflow, Prefect, or similar orchestration tools).
• Experience with GPU-accelerated computing (CUDA, Vulkan compute shaders).
• Familiarity with laboratory hardware integration (DAQ systems, TTL synchronization, serial/SPI protocols).
• Contributions to open-source neuroscience or scientific computing projects.

Education

Backgrounds in computer science, electrical engineering, biomedical engineering, neuroscience, physics, or related fields are all welcome. Graduate work or research experience in neuroscience or a related discipline is a plus but not required.