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Machine Learning Engineer Hybrid Jobs in Springfield, IL

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Information Security Architect

Springfield, IL · On-site +1

$61.43 - $92.15/hr

Collaborating with IT, clinical engineering, compliance, privacy, and business teams, the role ... machine-learning technologies, to assist with certain administrative aspects of the recruitment ...

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All JobsMachine Learning Engineer Hybrid JobsMachine Learning Engineer Hybrid Jobs in Springfield, IL

Machine Learning Engineer Hybrid information

See Springfield, IL salary details

$31.2K

$127.6K

$191.8K

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

As of Jul 22, 2026, the average yearly pay for machine learning engineer hybrid in Springfield, IL is $127,624.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,600.00 and $153,600.00 per year, depending on experience, location, and employer.

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

AspectMachine Learning Engineer HybridData Scientist
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops, tests, deploys ML models; collaborates with engineering teamsAnalyzes data, builds models, interprets results; works across departments
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

Machine Learning Engineer Hybrid focuses on developing and deploying ML models within engineering environments, often requiring coding and deployment skills. Data Scientists analyze data, build models, and interpret results, often in research or strategic roles. While both roles require strong analytical skills and knowledge of ML, the Engineer Hybrid emphasizes deployment and integration, whereas Data Scientists focus on data analysis and insights.

What are popular job titles related to Machine Learning Engineer Hybrid jobs in Springfield, IL? For Machine Learning Engineer Hybrid jobs in Springfield, IL, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Hybrid jobs in Springfield, IL look for? The top searched job categories for Machine Learning Engineer Hybrid jobs in Springfield, IL are:
Machine Learning Engineer Hybrid jobs near you
Infographic showing various Machine Learning Engineer Hybrid job openings in Springfield, IL as of July 2026, with employment types broken down into 89% Full Time, 9% Part Time, and 2% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $127,624 per year, or $61.4 per hour.

Machine Learning Engineer

Bespoke Labs

Springfield, IL • On-site

Full-time

Posted 5 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

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