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Machine Learning Engineer New Grad Jobs in Minneapolis, MN

* 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 ...

* 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 ...

Machine Learning Tutor

Edina, MN ยท Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Alaska, California, Colorado, Delaware, Hawaii, Maine, New Hampshire, North Dakota, Vermont, West ...

Machine Learning Tutor

Saint Paul, MN ยท Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Alaska, California, Colorado, Delaware, Hawaii, Maine, New Hampshire, North Dakota, Vermont, West ...

Machine Learning Tutor

Minneapolis, MN ยท Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Alaska, California, Colorado, Delaware, Hawaii, Maine, New Hampshire, North Dakota, Vermont, West ...

We're looking for a Principal Machine Learning Engineer to build AI features for the family ... Up to a 4% match with immediate vesting. * 12 weeks of paid leave for all new parents. * Learning ...

We're looking for a Principal Machine Learning Engineer to build AI features for the family ... Up to a 4% match with immediate vesting. * 12 weeks of paid leave for all new parents. * Learning ...

Showing results 21-40

Machine Learning Engineer New Grad information

See Minneapolis, MN salary details

$32.9K

$134.4K

$202K

How much do machine learning engineer new grad jobs pay per year?

As of Sep 3, 2026, the average yearly pay for machine learning engineer new grad in Minneapolis, MN is $134,409.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,900.00 and $161,800.00 per year, depending on experience, location, and employer.

What is a machine learning engineer new grad?

A Machine Learning Engineer New Grad job is an entry-level role for recent graduates specializing in machine learning and artificial intelligence. It typically involves developing, training, and deploying machine learning models, working with large datasets, and optimizing algorithms for performance. New grads in this role often collaborate with data scientists, software engineers, and product teams to integrate models into applications. Employers look for proficiency in programming (Python, TensorFlow, PyTorch), a strong foundation in ML concepts, and experience with data processing. This role provides an opportunity to gain hands-on industry experience and grow technical skills in real-world applications.

What are the typical day-to-day tasks of a machine learning engineer new grad?

As a Machine Learning Engineer New Grad, your daily tasks often include collecting and preprocessing data, developing and testing machine learning models, and analyzing model performance. You may work closely with data scientists and software engineers to integrate models into production systems and address real-world business problems. Participating in team meetings, code reviews, and collaborative projects is common, providing opportunities to learn best practices and receive mentorship. This hands-on, varied workload helps you quickly build technical and collaborative skills early in your career.

What are the key skills and qualifications needed to thrive in the machine learning engineer new grad position, and why are they important?

To thrive as a Machine Learning Engineer New Grad, a strong background in computer science, statistics, and mathematics, often supported by a relevant degree, is essential. Familiarity with programming languages like Python or Java, machine learning frameworks (such as TensorFlow or PyTorch), and basic knowledge of data tools and cloud platforms is typically required. Effective problem-solving, eagerness to learn, and clear communication help new grads excel when collaborating on projects and learning from senior team members. These skills and qualities are vital for adapting quickly, contributing to team goals, and building a successful foundation in this fast-evolving technical field.

What are the most commonly searched types of Machine Learning Engineer New Grad jobs in Minneapolis, MN?

The most popular types of Machine Learning Engineer New Grad jobs in Minneapolis, MN are:

What are popular job titles related to Machine Learning Engineer New Grad jobs in Minneapolis, MN?

For Machine Learning Engineer New Grad jobs in Minneapolis, MN, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer New Grad jobs in Minneapolis, MN look for?

The top searched job categories for Machine Learning Engineer New Grad jobs in Minneapolis, MN are:

What cities near Minneapolis, MN are hiring for Machine Learning Engineer New Grad jobs?

Cities near Minneapolis, MN with the most Machine Learning Engineer New Grad job openings:

Infographic showing various Machine Learning Engineer New Grad job openings in Minneapolis, MN as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $134,409 per year, or $64.6 per hour.

Machine Learning Engineer

Bespoke Labs

Minneapolis, MN โ€ข On-site

Full-time

Re-posted 17 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