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Machine Learning Biomedical Engineer Jobs in Flint, MI

Bespoke Labs

Machine Learning Engineer

Flint, MI · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

MERSEN USA HOLDING CORP.

Machine Operator

Metamora, MI

$15.50 - $18.50/hr

Knowledge of G-Coding programming, desired. * Ability to read complex blueprints and understand GD ... Learn and grow with us through our Learning and Development training programs * We champion ...

Oerlikon

Machine Operator

Lake Orion, MI · On-site

$15.75 - $18.75/hr

Learning fixturing and defixturing techniques. * Maintaining and updating equipment records ... With a unique portfolio spanning surface engineering, high-performance materials, coating equipment ...

Aerotek

Maintenance Engineer

Flint, MI

... and learning proprietary automation software. This position is highly client-facing and combines ... Strong troubleshooting skills for both mechanical and electrical issues on complex machinery.

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All JobsMachine Learning Biomedical Engineer JobsMachine Learning Biomedical Engineer Jobs in Flint, MI

Machine Learning Biomedical Engineer information

See Flint, MI salary details

$30.6K

$125.3K

$188.2K

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

As of Jul 8, 2026, the average yearly pay for machine learning biomedical engineer in Flint, MI is $125,260.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,700.00 and $150,800.00 per year, depending on experience, location, and employer.

What is the difference between Machine Learning Biomedical Engineer vs Data Scientist in Biomedical Industry?

AspectMachine Learning Biomedical EngineerData Scientist in Biomedical Industry
Required CredentialsDegree in Biomedical Engineering, Computer Science, or related fields; knowledge of machine learning and biomedical dataDegree in Data Science, Statistics, or related fields; proficiency in data analysis and machine learning
Work EnvironmentResearch labs, healthcare institutions, biotech companiesHealthcare analytics firms, research institutions, biotech companies
Employer & Industry UsageDevelops algorithms for medical devices, diagnostics, and treatment planningAnalyzes biomedical data to inform clinical decisions, research, and product development

Both roles require expertise in machine learning and biomedical data, but Machine Learning Biomedical Engineers focus on developing algorithms for medical applications, while Data Scientists analyze biomedical data to support research and clinical decisions.

What does a Machine Learning Biomedical Engineer do?

A Machine Learning Biomedical Engineer applies machine learning techniques to solve problems in biology and medicine. They develop algorithms and models to analyze complex biomedical data, such as medical images, genetic information, or sensor readings. Their work supports advancements in diagnostics, treatment planning, and personalized medicine. Typically, they collaborate with clinicians, researchers, and other engineers to design systems that improve healthcare outcomes.

What are the key skills and qualifications needed to thrive as a Machine Learning Biomedical Engineer, and why are they important?

To thrive as a Machine Learning Biomedical Engineer, you need a strong background in biomedical engineering, data analysis, and machine learning, typically supported by a degree in biomedical engineering, computer science, or a related field. Familiarity with programming languages like Python or R, machine learning frameworks (e.g., TensorFlow, PyTorch), and experience with medical imaging or signal processing tools are commonly required. Critical thinking, problem-solving, and the ability to communicate complex technical concepts to interdisciplinary teams are vital soft skills. These abilities are crucial for developing innovative healthcare solutions, ensuring regulatory compliance, and bridging the gap between technology and medicine.

How does a Machine Learning Biomedical Engineer typically collaborate with clinicians and researchers in a healthcare setting?

Machine Learning Biomedical Engineers often work closely with clinicians and researchers to develop algorithms that solve real-world medical challenges. Collaboration usually involves understanding clinical needs, translating them into technical requirements, and iteratively refining models based on feedback from medical experts. Regular meetings, interdisciplinary project teams, and direct participation in data collection or validation studies are common. This collaborative environment ensures that technical solutions are both innovative and clinically relevant, making communication and adaptability essential skills.
What cities near Flint, MI are hiring for Machine Learning Biomedical Engineer jobs? Cities near Flint, MI with the most Machine Learning Biomedical Engineer job openings:
Machine Learning Biomedical Engineer jobs near you
Infographic showing various Machine Learning Biomedical Engineer job openings in Flint, MI as of July 2026, with employment types broken down into 2% Internship, 1% As Needed, 81% Full Time, 15% Part Time, and 1% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $125,260 per year, or $60.2 per hour.

Machine Learning Engineer

Bespoke Labs

Flint, MI • On-site

Full-time

Posted 21 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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