The roleClients 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 doBuild 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 on
• Solve data scarcity — simulation-based synthetic data pipelines for anomaly classes real factories are too good to produce often
• Push the system beyond its current computer-vision strength — deepen audio ML and/or SLAM-based worker–vehicle association, depending on your background
• Ship at the edge — own the anonymization models the privacy guarantees depend on; optimize everything for constrained GPU/CPU budgets on factory hardware

• What you getThe ML production culture of a company, shaped by you from the start — registry, tracking, evals, your way
• Multimodal problems (vision, audio, spatial) most teams only get one of, on data nobody else has
• Your models on real assembly lines at major OEMs within weeks, with measurable stakes

Where you'll be in 12 monthsEvery 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 areYou report the real number, especially when it's bad — client acceptance tests leave no room for flattering evals
• You build machines that build models: reproducibility over heroics
• You prefer solving a problem once, generally, over solving it five times quickly
• You explore broadly, then converge and commit
• You're creative about data scarcity — synthesis, augmentation, simulation