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Graduate Machine Learning Jobs (NOW HIRING)

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $180K/yr

Desired Qualifications * 2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing-or a strong recent graduate with ...

Alljoined

Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

You will design and implement advanced machine learning models for EEG-based neural decoding ... A graduate degree (M.S. or Ph.D.) in computer science or a related field-such as artificial ...

PayPal

Machine Learning Engineer

San Jose, CA · On-site

$117K - $138K/yr

This job will assist in designing, developing, and implementing machine learning models and ... Recent Graduate Position Information and Requirements: * This is a Recent Graduate Full-Time ...

PayPal

Machine Learning Engineer

Austin, TX · On-site

$117K - $138K/yr

This job will assist in designing, developing, and implementing machine learning models and ... Recent Graduate Position Information and Requirements: * This is a Recent Graduate Full-Time ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

Experience with reinforcement learning or contextual bandit systems gained through graduate ... Expertise in training, evaluating, tuning, and deploying machine learning models across deep ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

What You'll Do You'll develop machine learning models that move beyond experimentation and into ... Experience with reinforcement learning or contextual bandit systems gained through graduate ...

Socket.dev

Machine Learning Scientist

Golden, CO · On-site

$150 - $200/hr

Graduate degree in atmospheric science, meteorology, computer science, or a related quantitative field. * 2+ years of experience developing machine learning approaches for weather prediction or ...

Tomorrow.io

Machine Learning Scientist

Golden, CO · Remote

Graduate degree in atmospheric science, meteorology, computer science, or a related quantitative field. * 2+ years of experience developing machine learning approaches for weather prediction or ...

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Graduate Machine Learning information

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How much do graduate machine learning jobs pay per year?

As of Sep 8, 2026, the average yearly pay for graduate machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is a graduate machine learning?

A Graduate Machine Learning job is an entry-level role designed for recent graduates with a background in machine learning, data science, or a related field. It typically involves working on data-driven projects, developing machine learning models, and assisting in research or engineering tasks. Graduates may collaborate with data scientists, software engineers, and business teams to design algorithms, optimize models, and deploy AI solutions. This role helps build practical experience in applying ML techniques to real-world problems while contributing to the organization's AI initiatives.

What does the typical career progression look like for a graduate machine learning?

As a Graduate Machine Learning professional, you will usually begin your career by working on smaller projects or supporting senior scientists with data preparation, model training, and performance evaluations. Over time, as you gain experience and demonstrate technical proficiency, you’ll be given more complex, independent projects and may specialize in areas like natural language processing, computer vision, or deep learning. Many organizations provide opportunities for mentorship, professional development, and advanced certifications, paving the way for roles such as Machine Learning Engineer, Data Scientist, or Research Scientist. This path offers significant opportunities for growth, both in terms of technical expertise and leadership potential.

What are the key skills and qualifications needed to thrive in the graduate machine learning position, and why are they important?

To thrive as a Graduate Machine Learning professional, you need a solid understanding of statistics, data analysis, machine learning algorithms, and programming languages such as Python or R, typically supported by a relevant degree. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch), data visualization tools, and version control systems like Git is common. Strong problem-solving abilities, communication skills, and a collaborative mindset will help you stand out in team-based environments. These competencies are vital for effectively building, analyzing, and refining models to address real-world business challenges.

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Graduate Machine Learning jobs near you
Infographic showing various Graduate Machine Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer

Ann Arbor, MI • On-site

Mariana Minerals
See more jobs at Mariana Mineralsexternal

$120K - $180K/yr

Full-time

Re-posted 29 days ago

Key responsibilities

  • Run reinforcement learning experiments in physically realistic simulators of mineral processing operations and help improve control models.

  • Build and refine training environments, including reward functions, observations, and action logic, with guidance from senior engineers.

  • Write clean, well-tested code and contribute to services that deploy models into production.

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 Machine Learning Engineers 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 Machine Learning Engineer at Mariana, you'll help build and improve the machine learning systems that control our mineral refining facilities. You'll start with well-scoped problems inside our simulators and training pipelines-and ramp quickly toward owning models that run on real, operating plants. Your work won't live behind dashboards or proxy metrics; you'll see its impact in real recovery rates, energy consumption, reagent usage, and uptime.
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
  • Run reinforcement learning experiments in our physically realistic simulators of mineral processing operations, and help turn the results into better controllers.
  • Build and refine pieces of our training environments-reward functions, observations, and action logic-with guidance from senior engineers.
  • Train control models, track and interpret their performance, and dig into why a model underperforms.
  • Help close the gap between simulation and reality by comparing model behavior against real plant data and flagging where the physics diverges.
  • Write clean, well-tested code and contribute to the services that put models into production.
  • Partner with process and chemistry experts to understand the unit operations you're modeling.
Desired Qualifications
  • 2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing-or a strong recent graduate with demonstrated project depth.
  • Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
  • Proficiency in Python and comfort reading and debugging an existing codebase.
  • Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering from experts who will challenge your assumptions.
  • A self-starter who asks good questions, ships, and escalates blockers early.
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.
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!

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