1

Machine Learning Engineer Two Jobs in Roseville, 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 ...

* 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 ...

Showing results 21-40

Machine Learning Engineer Two information

See Roseville, MI salary details

$28.6K

$116.8K

$175.5K

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

As of Sep 7, 2026, the average yearly pay for machine learning engineer two in Roseville, MI is $116,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,100.00 and $140,600.00 per year, depending on experience, location, and employer.

Are machine learning engineers still in demand?

Yes, machine learning engineers are in high demand across various industries due to the increasing adoption of AI and data-driven solutions. They are sought after for their skills in algorithms, programming, and tools like Python and TensorFlow, with job growth expected to continue as AI applications expand.

What cities near Roseville, MI are hiring for Machine Learning Engineer Two jobs?

Cities near Roseville, MI with the most Machine Learning Engineer Two job openings:

Infographic showing various Machine Learning Engineer Two job openings in Roseville, MI as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $116,822 per year, or $56.2 per hour.

Machine Learning Engineer

Bespoke Labs

Rochester Hills, MI • On-site

Full-time

Re-posted 22 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