Senior Computer Vision Engineer

About Us

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.

This role is based out of the San Francisco Bay Area and expected to work on site from our Alameda headquarters 2-3 days a week.

• We are intentional. We prioritize and are thoughtful about how we use others’ time.
• We care for others. We prioritize safety both for patients and one another.
• We own outcomes, not just tasks. Our work demands the highest standards because it impacts real patients and real lives.
• Humility is a strength. We are honest about what we know and what we don’t know. Getting it right matters more than being right.

• 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.

• 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…