Machine Learning Engineering Manager, Industrial Autonomy & Applied ML

Mariana Minerals
San Francisco, Houston, Ann Arbor
Workplace: OnsiteFull timeUSD 176,000 - 212,000 annuallyFunction: Data Science & Machine LearningSkills: ["Communication","Technical leadership","Cross-functional collaboration","Player-coach mindset","Stakeholder management"]

Lead a player-coach team of ML engineers building the applied ML stack that powers industrial autonomy—covering perception and vision, sensor/robotics, and LLM/agentic workflows. Own the technical direction and quality bar for production-ready deployment, evaluation, monitoring, and MLOps infrastructure. Partner with ML and robotics technical product managers and software engineering to translate roadmaps into feasible plans and deliver reliable systems for real plant operations.

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Mariana Minerals
Mariana Minerals
20 hours ago

Machine Learning Engineering Manager, Industrial Autonomy & Applied ML

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Source: Company careers pageValidated by: Fursa AI
Last checked: 11 hours agoStatus: Live

Job Summary

Lead a player-coach team of ML engineers building the applied ML stack that powers industrial autonomy—covering perception and vision, sensor/robotics, and LLM/agentic workflows. Own the technical direction and quality bar for production-ready deployment, evaluation, monitoring, and MLOps infrastructure. Partner with ML and robotics technical product managers and software engineering to translate roadmaps into feasible plans and deliver reliable systems for real plant operations.
Location: San Francisco, Houston, Ann Arbor
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Manager level

Key Responsibilities

  • •Manage, coach, and grow a team of ML engineers across perception, robotics, agentic workflows, and ML platform (hiring, onboarding, 1:1s, performance reviews, career development).
  • •Own the team’s technical direction, including platform architecture and what “good enough to deploy” means for vision models, robots, and agents.
  • •Partner with technical product managers to translate product priorities into scoped engineering work, assess feasibility, and commit to delivery.
  • •Own the ML platform and MLOps stack used across the ML organization, including training infrastructure, evaluation, deployment, monitoring, and acceleration for shipping.
  • •Own production lifecycle and reliability for the team’s systems (deployment, monitoring, incident response, on-call) and engineering practices across perception/robotics and LLM/agentic tools.

Pay and Benefits

Salary: USD 176,000 - 212,000 annually
Equity and Bonus:Equity

Key Requirements

  • •6+ years in machine learning engineering, including 2+ years managing ML engineers with responsibility for hiring, performance, and growth.
  • •Hands-on track record shipping ML systems to production in at least two of: computer vision/perception, robotics or embedded ML, LLM-powered or agentic applications, ML platform/MLOps infrastructure.
  • •Strong software engineering fundamentals for building and operating production systems with reliability and clear interfaces.
  • •Working fluency with the current LLM/foundation-model landscape, including evaluation and when agentic approaches fit versus conventional software.
  • •Experience setting technical direction in an ambiguous, cross-functional environment and evaluating work you didn’t personally do.
Experience:Machine learningComputer visionRoboticsLLMAgentic workflowsMLOpsIndustrial automationHeavy industry
Skills:CommunicationTechnical leadershipCross-functional collaborationPlayer-coach mindsetStakeholder management
Tech Stack:Machine learningComputer visionPerceptionRoboticsLLMsAgentic workflowsMLOps

Company Brief

Mariana Minerals
Software-first, vertically integrated minerals company developing extraction, processing, and automation systems to supply critical minerals (lithium and others) for energy, AI, and defense technologies, and building commercial-scale lithium facilities from produced water.
Industry: Mining & Metals
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Series A
Headquarters: San Francisco, United States
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