Jobs / United States / Robert Bosch LLC
AI Expert_PS
Robert Bosch LLC · 🇺🇸 Wuxi, Jiangsu, CN
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 92 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 50 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
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Sponsor Radar — Robert Bosch LLC
The US Department of Labor certified 92 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 50 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Robert Bosch LLC →
About the role
Job Summary As an AI Expert in RBCD/TEF3, you will be the technical driver of equipment-centric enterprise AI in BBM-CN — designing, deploying, and applying AI where it creates measurable value: on and around our manufacturing equipment. You will work deep inside the factory, side-by-side with equipment, maintenance, and operations teams, identifying real problems at the machine and solving them end-to-end with AI. You own your deployment strategy: prioritising use cases by business value and feasibility — from predictive maintenance and anomaly detection to machine-integrated vision and LLM-based shop-floor assistance — always on a rock-solid technical foundation in machine learning, industrial data, and edge deployment, and aligned with Bosch enterprise AI platforms (e.g., M.AI.Co) and the plant’s IIoT and edge infrastructure. For you, AI is never the goal: every project starts from a real problem on the shop floor and ends with a measured, sustainable improvement in daily production. Roles and Responsibilities Equipment-Centric AI Strategy & Deployment • Define and continuously refine the TEF3 AI strategy and roadmap for equipment and manufacturing technology — prioritising use cases by business value, data feasibility, and scalability, aligned with plant and PS/CE direction. • Translate equipment pain points (downtime, scrap, cycle time, energy, maintenance cost) into clearly scoped AI use cases with quantified targets and business cases. • Drive end-to-end deployment: from single-line pilot to plant-wide rollout and multi-plant replication, with defined acceptance criteria, KPI tracking, and sustainable handover to operations and maintenance. • Cooperate with PS central departments and other plants to deploy standard AI solutions for equipment and to feed proven TEF3 solutions back into the standard portfolio. AI Application Development on the Shop Floor • Develop and deploy AI applications that solve concrete equipment problems: predictive maintenance (failure prediction, remaining useful life), anomaly detection on machine and process signals, process parameter optimisation, OEE improvement, and maintenance / spare-parts planning support. • Develop computer-vision solutions for equipment-integrated quality and condition monitoring (e.g., on-machine inspection, AOI support). • Build LLM-based applications that genuinely help the shop floor: retrieval-augmented assistants over equipment manuals, fault codes, and work instructions — applied where they clearly beat simpler solutions. • Work directly at the machine with maintenance and equipment teams: observe, measure, and validate that solutions address root causes rather than symptoms. Industrial Data & Deployment Foundation • Design and implement industrial data pipelines: machine data acquisition (PLC, CNC, sensors via OPC UA / MQTT / fieldbus), edge processing, and historian / IIoT platform integration, ensuring data quality and governance at the source. • Deploy AI on industrial edge devices and on-prem infrastructure: model optimisation (quantisation, ONNX, TensorRT), containerised serving, and robust integration into equipment environments. • Establish MLOps practices for equipment AI: monitoring, drift detection, retraining, and full lifecycle management in production. • Ensure IT/OT security and functional-safety requirements are respected in every deployment on and around equipment. Leadership, Scaling & Knowledge Sharing • Lead or accompany cross-functional project teams (equipment, maintenance, production, quality, IT) with full responsibility for costs, quality, and on-time delivery. • Coach equipment and maintenance engineers in data and AI literacy; build pragmatic, lasting AI competence within TEF3. • Monitor AI technology developments (founda