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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 see and act in our plants through sensors and robotic systems, they run as agents inside the tools the business uses every day, and they run on an ML platform that has to serve all of it.
We're hiring a Machine Learning Engineering Manager to lead the MLEs working on everything outside the chemistry and process models: perception and vision, sensor and robotics initiatives, LLM-powered and agentic workflows, and the ML platform and MLOps infrastructure the whole applied AI/ML organization depends on. You'll manage the people, own the technical quality of what ships, and partner with both Technical Product Managers — ML & Robotics, and Data & Analytics Platform — on what gets built and why.
This is a player-coach role with a wide surface area. The problems range from a camera on a conveyor to an agent answering an operator's question to the training and deployment infrastructure underneath both. Your job is to build a team of strong generalists, keep the platform coherent as the use cases multiply, and give the TPMs a counterpart who can say what is technically possible and what it will take.
What You'll Do
- 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, and the hard conversations when they're needed.
- Own the technical direction of the team: which approaches to bet on, how the platform is architected, and what "good enough to deploy" means for a vision model, a robot, or an agent.
- Partner with TPMs on the roadmap: translate product priorities into scoped engineering work, push back when the ask isn't feasible, and commit to what the team will deliver.
- Own the ML platform and MLOps stack as a product the rest of the ML org uses: training infrastructure, evaluation, deployment, monitoring, and the paved road that lets every MLE ship faster.
- Own the engineering practices for perception and robotics: data collection and labeling, evaluation on real plant conditions, safety and fallback behavior, and the path from a demo to a system operators trust.
- Own the engineering practices for LLM and agentic tools: evaluation, guardrails, cost, latency, and the discipline to know when an agent is the right answer and when conventional software is.
- Own the seam with the software engineering organization at the engineering level — shared infrastructure, interfaces, and ownership — so nothing falls in the gap between the two orgs.
- Own the production lifecycle of the team's systems — deployment, monitoring, incident response, on-call — and the reliability bar for systems that act in a plant.
- Stay hands-on enough to review the hardest work, unblock engineers, and prototype when the fastest path to an answer is to build it yourself.
- Own headcount planning and hiring for the team, and build a pipeline of ML engineers who can move between problem areas.
How You'll Operate
- Player-coach: You spend most of your time on people, priorities, and quality — and enough time in the code and the systems to keep your judgment sharp.
- Structure from ambiguity: Turn a loosely defined plant problem or internal-tool idea into scoped engineering work with clear success criteria, and align operators, MLEs, software engineers, and product behind it.
- Breadth with depth on demand: You're credible across perception, LLMs, and infrastructure, and you know when a problem needs a specialist.
- Clear commitments: Give the TPMs and the business honest estimates, visible tradeoffs, and early warning when something is slipping.
- Ecosystem fluency: Understand the Mariana ML, perception, robotics, and data ecosystem as a whole — and where it sits relative to the current frontier of model capability — so the team's bets stay well-calibrated.
What We're Looking For
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: you've built and operated production systems, you care about interfaces and reliability, and you can hold a design review with software engineers as a peer.
- Working fluency with the current LLM/foundation-model landscape — what the models can do today, how to evaluate them, and where agentic approaches genuinely fit versus conventional software.
- Track record of setting technical direction for a team and delivering against commitments in an ambiguous, cross-functional environment.
- Ability to evaluate work you didn't do — you can review a model, a pipeline, or a system design and give feedback that makes the engineer better.
- 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.
Why This Role
Most ML manager roles hand you one problem area and a platform someone else owns. This role hands you the perception systems that will see our plants, the robotic systems that will act in them, the agents that will run inside our tools, and the platform underneath all of it — at the moment they move from ad hoc experimentation into supported systems. At Mariana, you don't need to validate product-market fit, because we are the market: if your team builds something that works, it gets deployed.
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!
Compensation Range: $176K - $212K
Salary Context
This $176K-$212K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Mariana Minerals, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($194K) sits 8% below the category median. Disclosed range: $176K to $212K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Mariana Minerals AI Hiring
Mariana Minerals has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $208K - $212K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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