Job description

ABOUT MARIANA MINERALS Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. We’re reimagining the minerals supply chain by combining deep industry expertise with advanced software, automation, and data-driven decision-making. THE ROLE Mariana Minerals is a software-first, vertically integrated minerals company supplying the minerals critical to modern energy, AI, and defense technologies. Our ML systems don't live in a vacuum — they run chemistry and process simulators, see and act in our plants through sensors and robotic systems, and increasingly move toward autonomous industrial process and chemical operations. We're hiring a Technical Product Manager to be the single product owner for machine learning and industrial robotics — the ML platform, the simulators it runs, and the perception and robotics initiatives built on it — and to own the seam between our applied AI/ML organization and our software engineering organization. Today this work is split across technical leads with no one setting direction across the whole. Your job is to change that: decide what the ML organization pursues and why, get stakeholders aligned behind it, and make sure engineers can stay heads-down on the work that matters. Autonomy and robotics here mean industrial: models, perception systems, and robotic hardware that increasingly close the loop on how our plants and chemical processes run. WHAT YOU'LL DO - Talk to our internal teams constantly — operators, process engineers, and MLEs are your users, and their problems set the roadmap. - Own the roadmap for the ML platform: which capabilities it needs next, for which use cases, and why. - Write the product specs, KPIs, and success metrics that turn ambiguous ML and autonomy asks into scoped, shippable work. - Own prioritization and stakeholder alignment so MLEs stay focused on deep technical work. - Own the path to process autonomy — models that inform, and increasingly set, process and chemical operating decisions — and the simulators that underpin it. - Own the roadmap for vision, sensor, and robotics initiatives: which plant problems get a model or a robot first, and what "good enough to deploy" means for each. - Own the seam between the applied AI/ML org and the software engineering org, and the boundary with MarianaOS, so nothing falls in the gap. - Define what it takes for a model, simulator, or robot to be trusted in production, and be explicit about which initiatives won't be pursued. HOW YOU'LL OPERATE - Structure from ambiguity: Bring direction to cross-disciplinary initiatives and align stakeholders rather than waiting for direction. - Ruthless, visible prioritization: Be explicit about what will not be worked on, not just what will. - Hybrid Pioneer/Settler: Prioritize and ship lightweight demos on ambiguous problems while bringing structure to existing work streams that lack cohesive ownership. - Ecosystem fluency: Understand the Mariana ML, perception, and robotics ecosystem as a whole — including where it sits relative to the current frontier of model capability — so prioritization calls stay well-calibrated. WHAT WE'RE LOOKING FOR Must have - 4–8+ years in technical product management, ML platform or robotics product roles, or equivalent experience leading cross-disciplinary technical programs - Comfortable being the only PM in a highly technical room — you know when to drive a decision, when to defer to engineers, and how to build credibility without being the most technical person there - Enough depth in ML systems — training, evaluation, deployment, perception, simulation — to earn credibility with both ML engineers and software engineers, and to know when a technical answer is a good one - Track record of bringing structure to ambiguous, cross-functional initiatives: setting direction, aligning stakeholders, and owning outcomes end-to-end - Strong prioritization and tradeoff skills — you know when to say no, and you say it visibly - Exceptional written and verbal communication across audiences, from ML engineers to operators to executives Nice to have - Product ownership of an ML platform, MLOps stack, or simulation system in production - Prior work on industrial robotics, perception, or closed-loop optimization of chemical or industrial process systems - Familiarity with process simulation toolchains (SysCAD or similar) - Background in mining, energy, chemicals, manufacturing, or other heavy industry — especially industrial automation or sensor/vision data - Working fluency with the current LLM/foundation-model landscape and where it applies to ML and robotics workflows WHY THIS ROLE Most ML PM roles hand you one model with an established roadmap. This role hands you the ML platform's path to autonomous process and chemical operations, the simulators that underpin it, the robotic systems that act on it, and the infrastructure it all runs on, at the moment they move from ad hoc experimentation and informal ownership into supported software. At Mariana, you don't need to validate product-market fit, because we are the market: if you identify a real problem and build a real solution, adoption is a conversation, not a sales cycle. OUR CULTURE IS BUILT ON FOUR PRINCIPLES: Everyone Gets Home Safe. We never put speed or cost ahead of people. Extreme Ownership. We take full responsibility for outcomes, relentlessly driving toward solutions. Engineer Out Requirements, then Automate. We simplify, optimize, and then automate for scale. Share Your Legos. We collaborate openly, share knowledge, and empower each other to build bigger, better solutions. Join us as we build the future of responsible mineral sourcing and supply!

Requirements

Must-haves

Autonomy and robotics here mean industrial: models, perception systems, and robotic hardware that increasingly close the loop on how our plants and chemical processes run.
Talk to our internal teams constantly — operators, process engineers, and MLEs are your users, and their problems set the roadmap.
Own the roadmap for the ML platform: which capabilities it needs next, for which use cases, and why.
Write the product specs, KPIs, and success metrics that turn ambiguous ML and autonomy asks into scoped, shippable work.
Own prioritization and stakeholder alignment so MLEs stay focused on deep technical work.
Own the path to process autonomy — models that inform, and increasingly set, process and chemical operating decisions — and the simulators that underpin it.
Own the roadmap for vision, sensor, and robotics initiatives: which plant problems get a model or a robot first, and what "good enough to deploy" means for each.
Own the seam between the applied AI/ML org and the software engineering org, and the boundary with MarianaOS, so nothing falls in the gap.
Define what it takes for a model, simulator, or robot to be trusted in production, and be explicit about which initiatives won't be pursued.
Structure from ambiguity: Bring direction to cross-disciplinary initiatives and align stakeholders rather than waiting for direction.
Ruthless, visible prioritization: Be explicit about what will not be worked on, not just what will.
Hybrid Pioneer/Settler: Prioritize and ship lightweight demos on ambiguous problems while bringing structure to existing work streams that lack cohesive ownership.
Ecosystem fluency: Understand the Mariana ML, perception, and robotics ecosystem as a whole — including where it sits relative to the current frontier of model capability — so prioritization calls stay well-calibrated.
4–8+ years in technical product management, ML platform or robotics product roles, or equivalent experience leading cross-disciplinary technical programs
Comfortable being the only PM in a highly technical room — you know when to drive a decision, when to defer to engineers, and how to build credibility without being the most technical person there
Enough depth in ML systems — training, evaluation, deployment, perception, simulation — to earn credibility with both ML engineers and software engineers, and to know when a technical answer is a good one
Track record of bringing structure to ambiguous, cross-functional initiatives: setting direction, aligning stakeholders, and owning outcomes end-to-end
Strong prioritization and tradeoff skills — you know when to say no, and you say it visibly
Exceptional written and verbal communication across audiences, from ML engineers to operators to executives

Nice-to-haves

Product ownership of an ML platform, MLOps stack, or simulation system in production
Prior work on industrial robotics, perception, or closed-loop optimization of chemical or industrial process systems
Familiarity with process simulation toolchains (SysCAD or similar)
Background in mining, energy, chemicals, manufacturing, or other heavy industry — especially industrial automation or sensor/vision data
Working fluency with the current LLM/foundation-model landscape and where it applies to ML and robotics workflows

Application prep

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Keywords to mirror

aimlmachine learningllm

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