SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Mariana Minerals is a software-first, vertically integrated minerals company supplying critical minerals for modern energy, AI, and defense technologies. The company is reimagining the minerals supply chain through advanced software, automation, and data-driven decision-making.
You will lead a team of ML engineers working across perception and vision systems, sensor and robotics initiatives, LLM-powered and agentic workflows, and the ML platform and MLOps infrastructure serving the entire applied AI/ML organization. This is a player-coach role with broad technical scope and significant people-management responsibility.
Key responsibilities include:
- Managing and growing a team of ML engineers across perception, robotics, agentic workflows, and ML platform infrastructure; handling hiring, onboarding, 1:1s, performance reviews, and career development
- Owning the technical direction of the team: architectural decisions, deployment readiness criteria for vision models, robots, and agents
- Partnering with Technical Product Managers on roadmap translation, feasibility assessment, and delivery commitments
- Owning the ML platform and MLOps stack as a product for the broader ML organization: training infrastructure, evaluation, deployment, monitoring, and developer velocity
- Establishing engineering practices for perception and robotics: data collection, labeling, evaluation on real plant conditions, safety protocols, and the path from prototype to production
- Establishing engineering practices for LLM and agentic tools: evaluation frameworks, guardrails, cost and latency optimization, and disciplined decision-making on when agents are appropriate
- Owning the interface with software engineering: shared infrastructure, interfaces, and clear ownership boundaries
- Owning production lifecycle and reliability: deployment, monitoring, incident response, on-call rotation, and reliability standards for plant-operating systems
- Staying hands-on enough to review complex work, unblock engineers, and prototype when necessary
- Owning headcount planning and building a pipeline of ML engineers across problem areas
You will operate as a player-coach, spending most time on people, priorities, and quality while maintaining enough hands-on involvement to keep technical judgment sharp. You'll structure ambiguous plant problems and internal-tool ideas into scoped engineering work with clear success criteria. You'll demonstrate breadth across perception, LLMs, and infrastructure while knowing when specialist depth is needed. You'll provide honest estimates, visible tradeoffs, and early warning signals to product and business partners. You'll maintain fluency with the broader ML, perception, robotics, and data ecosystem to keep the team's technical bets well-calibrated.
REQUIREMENTS:
Must have:
- 6+ years in machine learning engineering, including 2+ years managing ML engineers with direct 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: built and operated production systems, care about interfaces and reliability, can conduct design reviews with software engineers as a peer
- Working fluency with current LLM/foundation-model landscape: what models can do today, how to evaluate them, where agentic approaches genuinely fit versus conventional software
- Track record of setting technical direction for a team and delivering against commitments in ambiguous, cross-functional environments
- Ability to evaluate work you didn't do: can review models, pipelines, and system designs and provide feedback that improves engineer capability
- Exceptional written and verbal communication across audiences from ML engineers to operators to executives
Nice to have:
- Prior work on industrial robotics, perception in harsh environments, or closed-loop control of physical systems
- Experience owning an ML platform or MLOps stack used by multiple teams
- Experience shipping LLM-powered or agentic internal tools to real users and measuring adoption
- Background in mining, energy, chemicals, manufacturing, or other heavy industry, especially industrial automation or sensor/vision data
- Experience building a team from a handful of engineers to a functioning organization
