AI​/ML Team Leader

TL;

DR

We're hiring a Leader for our AI / ML / Data Science team (US, California Bay Area or Houston preferred) to build and ship production‑grade ML for industrial time‑series and multimodal data. You'll spend most of your time hands‑on (about 70%) delivering end‑to‑end solutions with customers, and the rest leading and scaling the data science team (about 30%). Position reports to the CTO and will have two direct reports.

We're hiring a Leader for our Data Science (DS) team to deliver production‑grade ML for industrial systems using time‑series and multimodal data (sensor/SCADA/historian, events, maintenance logs, and images/video where relevant). This is a hands‑on role where you'll spend most of your time building and shipping (roughly 70% IC), while also leading and growing the team (roughly 30% people leadership).

You'll work closely with customers and domain SMEs, ship models to production, and evolve our concept from “models” to an autonomous decisioning system: forecasting → detection/diagnosis → optimization → closed‑loop actions (with safety + governance). A key part of the role is advancing our unique IP in closed‑loop autonomous operations.

• Own the end‑to‑end lifecycle: problem framing → data readiness → modeling → deployment → monitoring → iteration.
• Define and execute roadmap areas like anomaly/event detection, asset/process health, root‑cause support, optimization, and closed‑loop decision support.
• Build scalable foundations for baselines, drift detection, model observability, and incident response.
• Partner with industrial customers and SMEs to translate real process constraints into ML/optimization/decisioning solutions. Drive unsupervised/self‑supervised initiatives (representations, clustering, change‑point detection, weak supervision, active learning).
• Develop a practical Reinforcement Learning (RL)/decisioning strategy (offline/safe RL, constrained optimization, simulators/digital twins), with guarded rollout patterns.
• Lead and mentor DS talent, set processes, frameworks and quality standards (design/code reviews, documentation, postmortems).
• Build and deploy AI / ML solutions / models in production. Own deployment, monitoring, performance validation, and iteration of models in production.
• Identify, document, and progress patentable innovations tied to closed‑loop autonomy and production deployment.

• 8+ years in applied AI / ML technologies, including 2+ years leading teams (hiring, mentorship, performance management).
• Deep experience with time‑series ML at scale, ideally with messy industrial data [Ex:
  Frequency‑domain time‑series techniques (FFT/spectral analysis) and control/optimization methods (MPC‑like approaches)].
• Proven track record of shipping and operating AI / ML solutions in production (MLOps, monitoring, drift, retraining, reliability).
• Strong Python and engineering fundamentals (clean code, testing, production patterns).
• Strong communication, comfortable working directly with customers and cross‑functional teams.

• Offline/safe RL, constrained optimization, and/or simulators/digital twins.
• Self‑supervised learning or foundation‑model approaches for industrial time‑series and multimodal fusion.
• Robotics and / or Industrial domain experience (manufacturing, energy, chemicals, mining, utilities), including safety/uptime/latency/edge constraints.
• Closed‑loop or human‑in‑the‑loop decision systems with governance and guardrails.
• Experience contributing to IP strategy, invention disclosures, and patent filings.

• Owning core product UI/UX design or front‑end development.
• Acting as the sole data engineer for ingestion/ETL across all customers (you'll partner closely with Engineering/Data Engineering where applicable).
• Running IT/OT infrastructure, sensor hardware selection, or plant networking.
• Doing research with no production path, success is measured in deployed outcomes and customer impact.
• Being a full‑time program manager, you will lead execution, but Delivery/CS/PM functions help run the overall program cadence.

• Customer impact:
  Delivered measurable improvements tied to customer KPIs…