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Machine Learning Engineer Hybrid Jobs in Ohio (NOW HIRING)

Bespoke Labs

Machine Learning Engineer

Columbus, OH · On-site

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 ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

The Hartford

Sr AI Machine Learning Engineer

Columbus, OH · Hybrid

$117K - $175K/yr

The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations ... Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Icehouseventures

$140 - $210/hr

... learning systems and can demonstrate strong applied ML engineering (not research-only). You have ... We operate a hybrid working model that combines in-person collaboration in our dedicated office ...

Quora

$139K - $168K/yr

Our team of Machine Learning Engineers have high impact by advancing the current Machine Learning systems, building performant and reliable LLM applications and collaborating with our product team to ...

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Machine Learning Engineer Hybrid information

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.

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Machine Learning Engineer Hybrid jobs near you

Machine Learning Engineer

Bespoke Labs

Columbus, OH • On-site

Full-time

Re-posted 24 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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