1

Ml Engineer Jobs in Michigan (NOW HIRING)

They are seeking an ML Engineer to build the intelligence layer of the company, focusing on machine learning models for production optimization and data infrastructure. Responsibilities : • Own ML ...

Senior AI/ML Engineer

Dearborn Heights, MI · On-site

$96K - $132K/yr

Senior AI/ML Engineer (W2 Position) Location: Dearborn, MI (Hybrid) Duration: 12+ Months Experience: 5+ Years JD: Skills Required: * Artificial Intelligence & Expert Systems, Machine Learning, Data ...

Collaborate with ML and Autonomy engineers to ensure the seamless deployment and validation of mapping features to the vehicle fleet. * Stay at the research frontier by evaluating, adapting, and ...

Senior, ML Engineer - VLM

Ann Arbor, MI · On-site +1

$102K - $140K/yr

Mentors and guides engineers within the group. * Bachelor's Degree in Computer Science, Robotics ... Distributed ML & data frameworks - PyTorch, Lightning, Ray, Spark, or equivalent for training and ...

Senior, ML Engineer - VLM

Ann Arbor, MI · On-site +1

$102K - $140K/yr

Mentors and guides engineers within the group. * Bachelor's Degree in Computer Science, Robotics ... Distributed ML & data frameworks - PyTorch, Lightning, Ray, Spark, or equivalent for training and ...

next page

Showing results 1-20

Ml Engineer information

See Michigan salary details

$28.8K

$77.7K

$123.8K

How much do ml engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for ml engineer in Michigan is $77,732.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,000.00 and $95,000.00 per year, depending on experience, location, and employer.

What is an ML engineer?

ML Engineers, or Machine Learning Engineers, are professionals who design, build, and deploy machine learning models into production systems. They bridge the gap between data science and software engineering, ensuring that machine learning solutions are scalable, reliable, and efficient. ML Engineers work with large datasets, develop algorithms, and optimize models for performance. They also collaborate with data scientists, software developers, and business stakeholders to solve real-world problems using artificial intelligence.

What are the key skills and qualifications needed to thrive as an ML engineer?

To thrive as an ML Engineer, you need a solid background in mathematics, statistics, computer science, and experience with machine learning algorithms, often supported by a degree in a related field. Familiarity with programming languages like Python or R, ML frameworks such as TensorFlow or PyTorch, and data processing tools is typically required, with relevant certifications being a plus. Strong problem-solving, critical thinking, and communication skills help you translate complex data insights into actionable solutions and work effectively in teams. These abilities ensure accurate model development, effective deployment, and successful collaboration on data-driven projects.

What are some common challenges ML engineers face when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring models remain accurate over time as data changes (known as data drift), optimizing models for speed and scalability, and integrating models seamlessly with existing software systems. Additionally, maintaining model performance in real-world environments can require continuous monitoring, retraining, and close collaboration with data engineers and DevOps teams. Addressing these challenges typically involves robust testing, using automated pipelines, and staying up-to-date with the latest MLOps best practices.

What is the difference between Ml Engineer vs Data Scientist?

AspectML EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, Data Science, or related fields; knowledge of ML frameworksBachelor's or Master's in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentDevelops, deploys, and maintains ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, startups, and enterprises deploying ML solutionsResearch institutions, tech firms, and industries relying on data analysis

While both roles involve working with data and machine learning, ML Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights to inform business decisions. The roles often overlap but differ in their core responsibilities and focus areas.

Are machine learning engineers still in demand?

Machine learning engineers are currently in high demand due to the growth of AI and data-driven technologies across industries. They typically require skills in programming, data analysis, and frameworks like TensorFlow or PyTorch, and often work in environments that emphasize continuous learning and adaptation. The demand is expected to remain strong as organizations increasingly rely on machine learning solutions for competitive advantage.

What does a machine learning engineer do?

A machine learning engineer designs, develops, and deploys machine learning models to solve specific problems using large datasets. They work with programming languages like Python or Java, utilize frameworks such as TensorFlow or PyTorch, and often collaborate with data scientists and software engineers to integrate models into applications.

What are the most commonly searched types of Ml Engineer jobs in Michigan?

The most popular types of Ml Engineer jobs in Michigan are:

What cities in Michigan are hiring for Ml Engineer jobs?

Cities in Michigan with the most Ml Engineer job openings:

Infographic showing various Ml Engineer job openings in Michigan as of August 2026, with employment types broken down into 87% Full Time, 6% Part Time, 2% Temporary, 4% Contract, and 1% Nights. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $77,732 per year, or $37.4 per hour.

Full-time

Re-posted 29 days ago


Job description

Job Summary:
Nox Metals is a technology company in Detroit supplying aluminum products to aerospace and defense manufacturers. They are seeking an ML Engineer to build the intelligence layer of the company, focusing on machine learning models for production optimization and data infrastructure.
Responsibilities:
• Own ML end to end across applied production work, novel research, and production deployment
• Build cut-time models that predict how long every cut takes by alloy, thickness, geometry, and machine
• Build nesting scoring and optimization models that make WAYNE smarter every day
• Build demand intelligence and procurement models that tell us what to buy, when, and at what price
• Build NLP and LLM features for sales order parsing, RFQ ingestion, and customer communication
• Build vision and OCR systems for cert package automation and dimensional inspection
• Ship features end to end, model, API, UI, deploy, monitor, no handoffs, no waiting
• Own the full ML lifecycle, data collection, labeling, training, evaluation, deployment, monitoring, retraining
• Build the data infrastructure and feature pipelines that future ML work depends on
• Partner with the software team to embed ML into NOX NEST, WAYNE, and Gondor
• Walk the floor, talk to operators, schedulers, and buyers, your training data starts where the work happens
• Always ask questions, never guess when something is unclear
• Look at every system and figure out how to make it better
• Work safely every shift and hold your teammates to the same standard
Qualifications:
Required:
• 5+ years of ML engineering experience shipping production models to real users
• Deep technical foundation across applied ML, foundation models or novel architectures, and production deployment
• Strong in Python, modern ML frameworks (PyTorch, JAX, or comparable), and the surrounding tooling
• Comfortable shipping production code, you build the API, the integration, and the UI when that is what the job needs
• Fluent in MLOps, model monitoring, retraining, and the unglamorous work of keeping models alive in production
• Experienced collecting, labeling, and structuring training data from messy real-world systems
• Cracked with AI tools, modern frameworks, and data, you move 10x faster than engineers who do not
• A builder at heart, you would rather ship a working v1 today than ship a perfect v3 next month
• High attention to quality, every model, every dataset, every deployment
• Always thinking about how to make systems better, you do not accept 'this is how we have always done it'
• Absolutely customer obsessed, every model eventually shows up as a part on a customer's dock
• User obsessed, you sit with the operator, the scheduler, the buyer, you watch them work, and you build models they actually trust
• A team player with a good attitude, you make the team better for everyone around you
• Someone who takes ownership, if it is in production, it is your responsibility
• Precise under pressure and reliable
• Organized and detail oriented
• Committed to safety, you follow every protocol, wear your PPE, and never cut corners that put people at risk
• High agency, you handle big items alone and ask for help when needed
• Low ego, you walk the floor, you talk to operators, you do the unglamorous work because it needs to get done
• Not afraid to work outside normal hours when America demands it
• Never says 'that's not my job'
Preferred:
• Experience building ML for manufacturing, logistics, supply chain, or industrial environments
• Strong vision and CV background, cert OCR, dimensional inspection, defect detection
• Strong optimization and operations research background, nesting, scheduling, routing
• Strong NLP and LLM background, structured extraction, agentic workflows, applied retrieval
• Experience integrating with hardware, PLCs, CNC controllers, or shop floor systems
• Open source work, published research, or a portfolio of things you have built
Company:
Nox Metals is an AI-powered metals supplier empowering modern American manufacturing at scale. Founded in 2025, the company is headquartered in Detroit, USA, with a team of 11-50 employees. The company is currently Early Stage.