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Machine Learning Engineer Associate Jobs in Minneapolis, MN

Varsity Tutors

Machine Learning Tutor

Edina, MN · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Saint Paul, MN · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Minneapolis, MN · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Staxa Technologies

AI engineer

Minneapolis, MN · On-site

$119K - $143K/yr

Job Overview We are seeking an experienced AI / Machine Learning Engineer to design, build, and deploy production-grade AI systems. In this role, you will bridge the gap between AI research and ...

NxT Level

Principal AI / ML Engineer

Minneapolis, MN · On-site +1

We're looking for a Principal Machine Learning Engineer to build AI features for the family. Qualifications: * A confident craftsperson who possesses problem-solving tools and can discuss multiple ...

NxT Level

Principal AI / ML Engineer

Minneapolis, MN · On-site

We're looking for a Principal Machine Learning Engineer to build AI features for the family. Qualifications: * A confident craftsperson who possesses problem-solving tools and can discuss multiple ...

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All JobsMachine Learning Engineer Associate JobsMachine Learning Engineer Associate Jobs in Minneapolis, MN

Machine Learning Engineer Associate information

See Minneapolis, MN salary details

$43.3K

$86.3K

$137.8K

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

As of Jul 9, 2026, the average yearly pay for machine learning engineer associate in Minneapolis, MN is $86,256.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,400.00 and $99,200.00 per year, depending on experience, location, and employer.

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 Machine Learning Engineer Associates?

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 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 Minneapolis, MN? The most popular types of Machine Learning Engineer jobs in Minneapolis, MN are:
What are popular job titles related to Machine Learning Engineer Associate jobs in Minneapolis, MN? For Machine Learning Engineer Associate jobs in Minneapolis, MN, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Associate jobs in Minneapolis, MN look for? The top searched job categories for Machine Learning Engineer Associate jobs in Minneapolis, MN are:
Machine Learning Engineer Associate jobs near you
Infographic showing various Machine Learning Engineer Associate job openings in Minneapolis, MN as of July 2026, with employment types broken down into 1% As Needed, 73% Full Time, 24% Part Time, 1% Temporary, and 1% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $86,256 per year, or $41.5 per hour.
Machine Learning Engineer- AI Data Platform (Minneapolis, MN)

Machine Learning Engineer- AI Data Platform (Minneapolis, MN)

MOBE, LLC

Minneapolis, MN • On-site

$119K - $143K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Company Overview

MOBE helps people discover new ways to live healthier. We are the whole-person, cross-condition solution that goes further to deliver better health and lower overall costs through evidence-based individual health guidance and pharmacist-led medication management. We empower individuals to make meaningful changes that improve their health and overall well-being. Behind our innovative solutions are robust data analytics, digital application, and a uniquely human philosophy. With one-to-one connection and compassion, we uncover opportunities, overcome challenges, and motivate people to transform their lives.

At MOBE our team is our most significant asset. We cultivate a culture grounded in curiosity, innovation, and growth. We encourage new ideas, fresh solutions, and meaningful impact. We value a workforce made up of people with differences who are eager to learn from each other and grow personally and professionally. We extend this approach to our partners and communities, seeking to increase understanding and expand opportunities across all groups.

Your role at MOBE

We are seeking a highly skilled AI Engineer to serve as a core builder of our AI Data Platform. This role sits at the intersection of machine learning engineering, data platform development, and business intelligence, with responsibility for designing and operating the infrastructure that powers AI-driven insights across the organization.

You will build intelligent data pipelines, production-grade ML systems, and AI-enabled features that translate complex data into actionable outcomes. This role is ideal for an engineer who enjoys working end-to-end from data ingestion and feature engineering to model deployment and downstream consumption in analytics and BI tools.

**Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Responsibilities:

  • Build AI-first data pipelines: Design, implement, and maintain scalable data pipelines that support model training, inference, and analytics use cases across the AI Data Platform.
  • Deploy production ML systems: Develop, deploy, and monitor machine learning models using AWS SageMaker, ensuring reliability, observability, and performance in production environments.
  • Implement Retrieval-Augmented Generation (RAG): Architect and maintain RAG-based systems that combine structured and unstructured data to power AI-driven insights and applications.
  • Operationalize ML lifecycle management: Use MLflow for experiment tracking, model versioning, and lifecycle management to support reproducibility and continuous improvement.
  • Design feature infrastructure: Build and manage feature stores (e.g., Feast, Tecton, or SageMaker Feature Store) to ensure consistent, reusable features across training and inference.
  • Orchestrate complex workflows: Create and manage Apache Airflow DAGs to orchestrate data transformations, model pipelines, and AI workflows with clear dependencies and monitoring.
  • Enable analytics consumption: Partner with BI and analytics teams to ensure ML outputs integrate cleanly with our internal BI reporting hub.
  • Translate business questions into AI solutions: Collaborate with stakeholders to convert ambiguous business problems into measurable ML- and data-driven solutions.
  • Uphold data quality and governance: Ensure AI pipelines and models adhere to data governance, security, and quality standards, particularly when handling sensitive data.
  • Collaborate cross-functionally: Work closely with Data Science, Analytics Engineering, Medical Economics, and DataOps to align AI platform capabilities with business priorities.

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