Senior Computer Vision Engineer

Epia Neuro•Alameda, CA

2d•$170,000 - $210,000•Hybrid

About The Position

Epia Neuro is a neural technology company developing intent-driven systems that restore function and independence for people living with neurological conditions. Our platform integrates implantable neural interfaces, adaptive algorithms, and assistive devices to translate neural intent into real-world action. Our initial focus is stroke-related motor impairment, with planned expansion into cognitive decline and other neurological disorders. We're looking for a Senior Computer Vision Engineer with deep computer vision (CV) and machine learning (ML) expertise, a hands-on approach, and a track record of taking models into production. You'll be the first dedicated computer vision engineer on the program. Partnering with our R&D scientists, you'll help shape vision models from concept, then lead their productization from validated prototype to a real-time inference runtime on wearable hardware, ready for clinical study. You'll report to the Senior Director of Software.

Requirements

Bachelor’s degree in Computer Science, Electrical Engineering, Robotics, Machine Learning, or related field.
7+ years of industry experience developing and deploying computer vision or machine learning systems.
Strong expertise in computer vision, including object detection, segmentation, and pose or keypoint estimation, with hands-on model training, evaluation, and dataset curation.
Demonstrated experience taking machine learning models from research prototype to a deployed, production system working under real constraints.
Hands-on experience with real-time and edge inference, including model optimization, latency and resource profiling, and deployment to embedded or mobile targets.
Production-quality Python, plus proficiency in C++ or embedded development for the inference runtime.
Experience with modern machine learning frameworks and deployment toolchains (e.g., PyTorch; ONNX, TensorRT, TFLite, or similar edge toolchains).
Excellent communication and cross-functional collaboration skills, with the ability to drive technical initiatives across teams.

Nice To Haves

PhD in Computer Vision, Machine Learning, Robotics, or related discipline.
Real-time computer vision for robotics, wearables, or other human-interactive systems.
Sensor fusion or visual servoing experience.
On-device or mobile machine learning, including Android.
Familiarity with safety-critical or regulated systems, such as medical devices (IEC 62304, ISO 13485, design controls) or other rigorous V&V environments.
Background or interest in rehabilitation, assistive technology, or neurotechnology.

Responsibilities

Collaborate with R&D scientists to develop computer vision models, taking them from concept through prototype.
Bring computer vision and machine learning depth to the research effort, including model design, training, and evaluation.
Help define data collection, annotation, and evaluation protocols, and establish performance criteria tied to clinical use.
Build prototype models and pipelines that are ready to carry into productization.
Lead and own productization of the computer vision models, taking validated prototypes to a deployable inference runtime that meets real-time latency and power budgets on wearable and edge hardware.
Optimize model performance, including quantization, pruning, and distillation.
Profile latency and resource use across the inference path.
Own integration of the models into the broader medical device product, working with Software, Firmware, Hardware, and Robotics teams.
Define and execute benchtop and system-level validation to characterize accuracy, robustness, and performance to clinical study readiness.
Lead debugging and root-cause analysis across the machine learning, firmware, and controls boundaries.
Drive technical decisions for system reliability and performance across prototype and pre-production builds.
Serve as the technical lead for computer vision engineering across the program.
Help establish machine learning engineering processes, documentation standards, and test methodologies within our regulated software lifecycle.
Partner with cross-functional stakeholders to define technical requirements, integration milestones, and validation criteria.
Support vendor selection, component evaluation, and design tradeoff analysis for cameras, compute, and computer vision/machine learning tooling.
Contribute to long-term computer vision/machine learning platform strategy.