AI/ML Engineer - SLAM and Audio Machine Learning

About the company Our client is building an AI assistant that runs on smart glasses to support assembly-line workers in real time — streaming and processing video, audio, and IMU sensor data live, and delivering audio feedback directly to the worker. The system is already deployed on production lines at major automotive OEMs, privacy-first by design: everything persisted is anonymised at ingestion. Long-term vision: the egocentric data collected is training fuel for the next generation of robot learning — positioning the company at the heart of the race toward autonomous robots for industrial deployment across automotive, aviation, aerospace, and beyond. The role Clients accept the system through formal tests with hard recall and false-positive gates — every model must provably work: which weights, trained on which data, with which config, always answerable. You'll own that machinery end-to-end, and alongside it, help push the system's audio and/or spatial-reasoning capabilities forward — knowing which vehicle a worker is acting on as they move between cars, using SLAM fused with vehicle identity signals. You'll work as a peer to the Lead ML Engineer — they own what the system should do, you own how models get built, trained, and reproduced — designing together, in the open, with a direct line to the CTO. What you'll do Build the machinery that makes models provable — training pipelines, experiment tracking, model registry: full lineage from dataset to deployed weights, plus the evaluation harnesses client acceptance is staked onSolve data scarcity — simulation-based synthetic data pipelines for anomaly classes real factories are too good to produce oftenPush the system beyond its current computer-vision strength — deepen audio ML and/or SLAM-based worker–vehicle association, depending on your backgroundShip at the edge — own the anonymization models the privacy guarantees depend on; optimize everything for constrained GPU/CPU budgets on factory hardware What you get The ML production culture of a company, shaped by you from the start — registry, tracking, evals, your wayMultimodal problems (vision, audio, spatial) most teams only get one of, on data nobody else hasYour models on real assembly lines at major OEMs within weeks, with measurable stakes Where you'll be in 12 months Every model that passes client acceptance is reproducible from the registry. A rare-anomaly class hit its recall gate on synthetic data. Depending on where you focus: audio is live in a deployment, or SLAM-based worker–vehicle association is validated on a real line — likely both, over time. Who you are You report the real number, especially when it's bad — client acceptance tests leave no room for flattering evalsYou build machines that build models: reproducibility over heroicsYou prefer solving a problem once, generally, over solving it five times quicklyYou explore broadly, then converge and commitYou're creative about data scarcity — synthesis, augmentation, simulation Your experience Must have: Strong PyTorch and production ML experience (detection / classification / tracking)Hands-on MLOps: experiment tracking, model registries, training pipelinesProven industry experience in either audio ML or SLAM/spatial perception in production — you don't need both, but at least one at real depthModel optimization for edge hardware (e.g. ONNX, TensorRT) Ways to stand out: Depth in the other of audio ML / SLAM, beyond your primary strengthSynthetic data generation (e.g. simulation, 3D rendering, generative augmentation)Manufacturing, robotics, or other physical-world domainsPhD (preferred, not required — strong industry track record matters more)