Principal Software Engineer, Neuroscience Platforms

Astera Institute•Emeryville, CA

1d•$200,000 - $350,000

About The Position

Astera Neuro is hiring software engineers to build the computational backbone of large-scale neuroscience research, from MRI-guided targeting and millisecond-precise stimulus and recording systems to high-throughput neural data pipelines. You don’t need a neuroscience background; you need to love hard technical problems and build fast. Let’s talk. At Astera Neuro, we are deciphering how activity across billions of neurons becomes thought, perception, and the conscious experience of the world and ourselves. Doing that requires building cutting-edge tools and infrastructure to interface with, record, and control neural activity at an unprecedented scale. We are hiring a Principal Software Engineer to lead the computational infrastructure that the next generation of large-scale neuroscience research will depend on. Working closely with scientists and hardware engineers, you will span the full stack of our research systems, including instrument control, data acquisition, and analysis, translating open-ended experimental goals into reliable software. The problems are latency-sensitive, multi-modal, and deeply interdisciplinary. You do not need a background in neuroscience to apply; we are looking for an exceptional, pragmatic engineer who enjoys solving hard, interesting problems and picks up the science as they go. As a principal engineer, you will define the technical roadmap across systems, mentor other engineers, and shape how neuroscience and engineering work together at Astera.

Requirements

Exceptional software engineering fundamentals, proven in production.
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 mentoring engineers and leading technical initiatives, with the judgment to set technical direction across teams.
Comfort working across the full stack, from low-level hardware interfaces to cloud infrastructure.

Nice To Haves

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.

Responsibilities

Define the technical roadmap across systems.
Mentor other engineers.
Shape how neuroscience and engineering work together at Astera.
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.
Build 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.
Build dashboards, user interfaces, and experiment-design tools that let scientists interact with complex data and run experiments seamlessly.
Build cloud-based storage, processing and sharing infrastructure that supports distributed teams and large-scale datasets.
Own complex systems end-to-end, from architecture and implementation through operational reliability.
Make and defend technical tradeoffs across performance, maintainability, and team velocity.
Build and iterate on human-facing research systems with speed and taste.