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Machine Learning Engineer Associate Jobs in Lansing, MI

Bespoke Labs

Machine Learning Engineer

Lansing, MI · On-site

* 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 ...

Bespoke Labs

Machine Learning Engineer

Lansing, MI

* 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 ...

IBM

Associate Data Engineer 2027 - AI & Analytics

Lansing, MI · On-site

$59K - $60K/yr

Your role and responsibilities As an Associate Data Engineer at IBM, you'll help organizations ... Foundational understanding of Artificial Intelligence, Machine Learning, Generative AI, or data ...

Software People, Inc.

AI Developer

Lansing, MI · On-site

The resource also provides technical oversight to developers in the team that support other ... Experience with machine learning algorithms and deep learning frameworks such as TensorFlow ...

Alro Steel Corp

Machining Engineer

Charlotte, MI · On-site

Coordinate machine readiness to minimize downtime and maximize productivity. * Support new product ... High school diploma or GED required; associate degree or technical certification in machining ...

Staffactory

Programmer Analyst 6

Lansing, MI · On-site

$58 - $63/hr

Position: Programmer Analyst 6 Location: Lansing, MI 48933 Minimum Education Level: Bachelor ... advanced analytics, machine learning, and real-time data processing initiatives. The ideal ...

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Machine Learning Engineer Associate information

See Lansing, MI salary details

$42.1K

$83.8K

$133.9K

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

As of Sep 10, 2026, the average yearly pay for machine learning engineer associate in Lansing, MI is $83,815.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,400.00 and $96,400.00 per year, depending on experience, location, and employer.

What is a machine learning engineer associate?

Machine Learning Engineer Associates are entry-level professionals who help design, build, and maintain machine learning models and systems. They typically work under the guidance of senior engineers, assisting in data preprocessing, model training, and testing. Their responsibilities may include implementing algorithms, evaluating model performance, and deploying solutions to production environments. This role requires a strong foundation in programming, statistics, and machine learning principles, often acquired through education or internships.

What are some common challenges faced by machine learning engineer associates when deploying models to production?

Machine Learning Engineer Associates often encounter challenges such as ensuring model scalability, managing data pipeline reliability, and addressing issues with model drift after deployment. Collaborating closely with data engineers and software developers is essential to integrate models seamlessly into existing systems. Additionally, balancing model performance with resource constraints and maintaining clear documentation for reproducibility are important aspects of the role. Gaining familiarity with deployment tools and best practices can help overcome these hurdles.

What are the key skills and qualifications needed to thrive as a machine learning engineer associate, and why are they important?

To thrive as a Machine Learning Engineer Associate, you need a solid understanding of programming (especially Python), mathematics, and foundational machine learning concepts, typically supported by a relevant degree or coursework. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and experience with version control systems such as Git are essential. Strong problem-solving abilities, communication skills, and a collaborative mindset help you work effectively within technical teams. These competencies ensure you can develop, implement, and improve machine learning models that deliver actionable insights and drive business value.

What are the most commonly searched types of Machine Learning Engineer jobs in Lansing, MI?

The most popular types of Machine Learning Engineer jobs in Lansing, MI are:

What are popular job titles related to Machine Learning Engineer Associate jobs in Lansing, MI?

For Machine Learning Engineer Associate jobs in Lansing, MI, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Associate jobs in Lansing, MI look for?

The top searched job categories for Machine Learning Engineer Associate jobs in Lansing, MI are:

What cities near Lansing, MI are hiring for Machine Learning Engineer Associate jobs?

Cities near Lansing, MI with the most Machine Learning Engineer Associate job openings:

Machine Learning Engineer Associate jobs near you
Infographic showing various Machine Learning Engineer Associate job openings in Lansing, MI as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 71% Full Time, 26% Part Time, and 1% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution, with an average salary of $83,815 per year, or $40.3 per hour.

Machine Learning Engineer

Lansing, MI • On-site

Bespoke Labs
See more jobs at Bespoke Labsexternal

Full-time

Re-posted 24 days ago

Job description

  • 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, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems

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