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Temporary Machine Learning Engineer Jobs in Lombard, IL

* 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 ...

* 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 ...

Salary Range $200,000-$225,000 USD About Us IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing ...

Senior AI Machine Learning Engineer

Chicago, IL · Hybrid

$126K - $166K/yr

As a Senior Machine Learning Engineer , you will play a critical role in designing, building, and operationalizing productiongrade AI solutions-partnering closely with product, engineering, and ...

... engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production * Track and evaluate emerging research in neural architecture search, machine learning ...

Salary Range $200,000-$225,000 USD About Us IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing ...

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Showing results 41-60

Temporary Machine Learning Engineer information

See Lombard, IL salary details

$31K

$126.7K

$190.4K

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

As of Sep 4, 2026, the average yearly pay for temporary machine learning engineer in Lombard, IL is $126,719.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,900.00 and $152,500.00 per year, depending on experience, location, and employer.

What is the difference between Temporary Machine Learning Engineer vs Data Scientist?

AspectTemporary Machine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related fields; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentProject-based, often contract roles in tech or finance companiesResearch and analysis-focused, in tech, finance, or healthcare sectors
Employer UsageUsed for short-term ML projects, model deployment, or prototypingUsed for data analysis, insights, and predictive modeling

Temporary Machine Learning Engineers focus on implementing and deploying ML models on a short-term basis, often within project deadlines. Data Scientists analyze data to generate insights and develop models but may have a broader scope. Both roles require strong technical skills, but their primary functions differ in scope and application.

What are popular job titles related to Temporary Machine Learning Engineer jobs in Lombard, IL?

For Temporary Machine Learning Engineer jobs in Lombard, IL, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Engineer jobs in Lombard, IL look for?

The top searched job categories for Temporary Machine Learning Engineer jobs in Lombard, IL are:

What cities near Lombard, IL are hiring for Temporary Machine Learning Engineer jobs?

Cities near Lombard, IL with the most Temporary Machine Learning Engineer job openings:

Machine Learning Engineer

Bespoke Labs

Aurora, IL • On-site

Full-time

Re-posted 19 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