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Temporary No Experience Machine Learning Jobs in Novi, MI

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 ...

Varsity Tutors

Machine Learning Tutor

Ann Arbor, MI · Remote

$18 - $40/hr

... Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ... No commuting required. * Get matched with students best-suited to your teaching style and expertise.

Varsity Tutors

Machine Learning Tutor

Detroit, MI · Remote

$18 - $40/hr

... Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ... No commuting required. * Get matched with students best-suited to your teaching style and expertise.

Stefanini

Machine Learning Engineer

Dearborn, MI · On-site

Stefanini is looking for a Machine Learning Engineer(Dearborn, MI) For quick apply, please reach ... Listed salary ranges may vary based on experience, qualifications, and local market. Also, some ...

Wayve

Machine Learning Engineer, App SW

Detroit, MI · Hybrid

$283K - $381K/yr

About you: In order to set you up for success as a Machine Learning Engineer at Wayve, we're ... Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop ...

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How much do temporary no experience machine learning jobs pay per hour?

As of Aug 24, 2026, the average hourly pay for temporary no experience machine learning in Novi, MI is $21.41, according to ZipRecruiter salary data. Most workers in this role earn between $18.51 and $23.89 per hour, depending on experience, location, and employer.

What is the difference between Temporary No Experience Machine Learning vs Data Analyst?

AspectTemporary No Experience Machine LearningData Analyst
Required CredentialsBasic understanding of programming, no formal certification neededDegree in data science, statistics, or related field; certifications optional
Work EnvironmentProject-based, often in tech or AI companies, collaborative teamsOffice or remote, analyzing data sets, reporting insights
Industry UsageTech, AI startups, research projectsBusiness, finance, marketing, healthcare
Search & Comparison IntentEntry-level, no experience, beginner machine learning rolesData analysis, reporting, data-driven decision making

Temporary No Experience Machine Learning roles focus on entry-level tasks with minimal credentials, often in tech environments. Data Analyst positions typically require some formal education and involve analyzing data to support business decisions. Both roles are common in data-driven industries but differ in skill requirements and daily tasks.

How to get into temporary no experience machine learning?

To enter a temporary machine learning role with no experience, focus on building foundational skills in programming (Python), data analysis, and basic machine learning concepts through online courses or tutorials. Gaining familiarity with tools like scikit-learn and participating in projects or internships can improve your chances, even without prior experience.

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Temporary No Experience Machine Learning jobs near you
Infographic showing various Temporary No Experience Machine Learning job openings in Novi, MI as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 22% Part Time, and 4% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $44,534 per year, or $21.4 per hour.

Machine Learning Engineer

Mariana Minerals

Ann Arbor, MI • On-site

$120K - $180K/yr

Full-time

Re-posted 14 days ago

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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