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Internship Machine Learning Compiler Engineer Jobs in Michigan

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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Internship Machine Learning Compiler Engineer information

What is the difference between Internship Machine Learning Compiler Engineer vs Internship Software Engineer?

AspectInternship Machine Learning Compiler EngineerInternship Software Engineer
FocusDeveloping and optimizing compilers for machine learning modelsDesigning, coding, and testing software applications across various domains
SkillsMachine learning, compiler design, programming (C++, Python)Programming, algorithms, software development
Work EnvironmentResearch labs, tech companies, AI-focused teamsTech companies, startups, software firms
Industry UsageAI, machine learning, deep learning industriesBroad software development across industries

Internship Machine Learning Compiler Engineers focus on creating and optimizing compilers for machine learning models, requiring knowledge of AI and compiler design. In contrast, Internship Software Engineers work on developing general software applications across various fields. Both roles involve programming skills but differ in their specialization and industry focus.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in Michigan? The most popular types of Machine Learning Compiler Engineer jobs in Michigan are:
What are popular job titles related to Internship Machine Learning Compiler Engineer jobs in Michigan? For Internship Machine Learning Compiler Engineer jobs in Michigan, the most frequently searched job titles are:
What cities in Michigan are hiring for Internship Machine Learning Compiler Engineer jobs? Cities in Michigan with the most Internship Machine Learning Compiler Engineer job openings:

Machine Learning Engineer

Bespoke Labs

Mount Pleasant, MI • On-site

Full-time

Posted 7 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