AI Engineer

As an AI Engineer, you will work with our CTO and senior engineers to improve the 2D/3D vision models behind 3DGait and bring small language model and RAG capabilities to constrained clinical hardware. You will also help build the testing, validation, and documentation healthcare products require.

Key Responsibilities

Model development: Design, train, and iterate on 2D/3D computer vision models for gait and movement analysis that power 3DGait’s core clinical capability.

Edge deployment: Build and deploy SLM-based RAG pipelines optimized for edge environments, including model compression, quantization, and latency and memory tuning for constrained clinical hardware.

Model quality assurance: Design test protocols, benchmarking suites, and accuracy and performance metrics that meet the standard of clinical scrutiny.

Regulatory support: Contribute technical documentation, validation evidence, and risk assessments aligned with SaMD requirements (FDA/HSA, ISO 13485/62304), working alongside our clinical and regulatory leads. Pipeline maintenance: Maintain the CI/CD and cloud deployment pipeline (AWS/GCP) used to release models.

Cross-functional collaboration: Work closely with backend, mobile, and clinical teams to ensure your work integrates seamlessly into the product used by clinicians. Documentation: Maintain clear records of what you have built, tested, and validated.

Qualifications

Required Strong Python skills, with hands-on experience building and training 2D/3D computer vision or deep learning models (e. g. PyTorch, TensorFlow). Familiarity with retrieval-augmented generation (RAG) pipelines. Coursework, side projects, and self-directed learning are all acceptable.

Preferred Experience deploying models to edge or embedded hardware (e. g. quantization, ONNX, CoreML, TensorRT) rather than cloud-only inference. Prior exposure to a regulated industry (healthcare or medtech), such as QA test design or contributing to regulatory documentation for a SaMD or similar produc