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Google Machine Learning Engineer Jobs in Michigan

Senior Machine Learning Engineer

Detroit, MI ยท On-site +1

$126K - $180K/yr

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director of AI Engineering, you'll contribute to the development of cutting-edge AI solutions to combat ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Showing results 21-40

Google Machine Learning Engineer information

See Michigan salary details

$27.5K

$112.2K

$168.7K

How much do google machine learning engineer jobs pay per year?

As of Sep 11, 2026, the average yearly pay for google machine learning engineer in Michigan is $112,234.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,500.00 and $135,100.00 per year, depending on experience, location, and employer.

What is a Google machine learning engineer?

A Google Machine Learning Engineer designs, builds, and optimizes machine learning models to improve Google's products and services. They work with large datasets, implement algorithms, and deploy scalable AI systems. Collaboration with data scientists, software engineers, and product teams is essential to integrate models into real-world applications. Strong knowledge of Python, TensorFlow, and cloud computing is often required. This role focuses on both research and practical implementation to enhance automation and decision-making across Google products.

What skills and qualifications are needed to thrive as a Google machine learning engineer?

To thrive as a Google Machine Learning Engineer, you need strong expertise in mathematics, statistics, programming (especially Python or C++), and a solid background in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), cloud platforms (like Google Cloud), and advanced certifications can be highly beneficial. Excellent problem-solving, teamwork, and communication skills help you collaborate across teams and explain complex models to stakeholders. These skills are essential to driving innovation, building scalable solutions, and ensuring impactful results in a fast-paced, research-driven environment.

What types of projects and collaborations can Google machine learning engineers expect to be involved in?

Google Machine Learning Engineers often contribute to diverse projects, such as developing next-generation search algorithms, optimizing user experiences across products, or creating scalable machine learning systems for internal and external clients. The role frequently involves collaborating with data scientists, product managers, software engineers, and researchers to define project goals and deliver impactful solutions. You can expect to participate in code reviews, prototype new models, and provide expert input during technical discussions. This collaborative, interdisciplinary approach ensures innovative outcomes and offers ongoing opportunities for professional growth and skill development.

What are the most commonly searched types of Google Machine Learning Engineer jobs in Michigan?

The most popular types of Google Machine Learning Engineer jobs in Michigan are:

What are popular job titles related to Google Machine Learning Engineer jobs in Michigan?

For Google Machine Learning Engineer jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Google Machine Learning Engineer jobs in Michigan look for?

The top searched job categories for Google Machine Learning Engineer jobs in Michigan are:

Infographic showing various Google Machine Learning Engineer job openings in Michigan as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 20% Part Time, 3% Contract, and 1% Nights. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution, with an average salary of $112,234 per year, or $54 per hour.

Staff Machine Learning Engineer

Ann Arbor, MI โ€ข On-site

$160K - $240K/yr

Full-time

Re-posted 2 days ago


Key responsibilities

  • Own the autonomy roadmap across multiple circuits and facilities, deciding which unit operations to automate next.

  • Define how control models are validated and certified safe to deploy on real refining equipment, including closing the gap between simulation and reality.

  • Solve the hardest modeling and control problems related to non-stationarity, safety constraints, and multi-objective optimization.


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 building the critical minerals supply chain from the ground up-and we're looking for a Staff Machine Learning Engineer to help make it autonomous.
We're not a software company selling tools to mining operators. We are a mining company that builds software. Mariana designs, builds, commissions, and operates our own mines and refineries. We develop proprietary chemical processes and run them at lab, pilot, and commercial scale. Today, we're producing battery-grade lithium salts from real oil and gas wastewater in our facilities. Our first commercial-scale lithium production facility, Lithium One, is targeting initial production in Q1 of 2027.
As a Staff Machine Learning Engineer at Mariana, you'll set the technical direction for how we make refining autonomous. You'll define how control models are built, validated, and trusted on live equipment across our circuits and facilities-and you'll personally take on the hardest modeling problems standing between us and fully autonomous operations. Your decisions will show up in real recovery rates, energy consumption, reagent usage, and uptime across every plant we run.
The Tech
This is some of the most interesting applied AI work happening today.
Our internal platform, uses the same reinforcement learning toolkits that power self-driving vehicles and humanoid robots-but applied to autonomous, short-interval control of mineral refining circuits. Models adjust operating set points and configurations in real time, optimizing across lithium recovery, reagent consumption, energy intensity, and equipment uptime simultaneously.
The environment is noisy and non-stationary: wastewater compositions shift, ore grades change, equipment ages. The system must continuously adapt. The end goal is fully autonomous refining operations. When you ship here, you can literally watch the physics change.
Under the hood, that means training control models inside physically realistic simulators of our process units, then closing the gap against real plant data before anything touches live equipment.
What You'll Do
  • Own the autonomy roadmap across multiple circuits and facilities-deciding which unit operations to automate next and where investment in simulation and modeling pays off.
  • Define how control models are validated and certified safe to deploy on real refining equipment, including how the gap between simulation and reality is measured and closed.
  • Set the standards for our simulators and our modeling stack, so the whole team builds controllers that are reproducible, safe, and grounded in real project economics.
  • Personally solve the hardest modeling and control problems-non-stationarity, safety constraints, and multi-objective optimization across recovery, reagent use, energy, and uptime.
  • Partner with leadership on major capital and operational decisions, translating techno-economic and process insight into strategy.
  • Multiply the team through technical direction, design review, and mentoring of engineers at every level-and partner with our data engineering leaders to shape the data platform the autonomy roadmap requires. You own the modeling and the on-plant outcome; they own the backbone.
Desired Qualifications
  • 8-10+ years in machine learning engineering (or an exceptional 6+ with demonstrated org-level technical leadership), including production ML or control systems that ran in the real world.
  • A track record of setting technical direction for ML systems in physical, industrial, robotics, or control domains.
  • Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps-plus the judgment to know when a simpler approach wins.
  • Demonstrated ability to de-risk ambiguous, never-been-done problems: framing the objective, the success metric, and the path for others.
  • Strong cross-functional influence with both technical leadership and domain experts-chemists, metallurgists, process engineers, and geologists.
  • A builder at heart. Staff engineers here still ship.
Why This Role
We own the projects, generate the data, and close the loop. Every facility we build makes the software smarter-and the next facility faster and cheaper.
Mining is one of the last major industrial sectors that hasn't been rebuilt with modern software. The opportunity here isn't a feature gap-it's entire workflows and systems that don't exist yet.
Your work will directly shape how critical minerals are produced at scale in the coming decades. This is a role for someone who wants to set the technical direction of an entire industrial-AI platform while it's still being invented-not maintain one that already exists.
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!