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Machine Learning Engineer Starting Jobs in Kentucky

Bespoke Labs

Machine Learning Engineer

Frankfort, KY · 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 ...

Bespoke Labs

Machine Learning Engineer

Louisville, KY · 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 ...

Bespoke Labs

Machine Learning Engineer

Owensboro, KY · 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 ...

Bespoke Labs

Machine Learning Engineer

Covington, KY · 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 ...

Bespoke Labs

Machine Learning Engineer

Lexington, KY · 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 ...

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Machine Learning Engineer Starting information

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$27.4K

$111.8K

$168.1K

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

As of Jul 21, 2026, the average yearly pay for machine learning engineer starting in Kentucky is $111,839.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,200.00 and $134,600.00 per year, depending on experience, location, and employer.

What engineer makes $500,000 a year?

Highly experienced machine learning engineers working in top tech companies or specialized fields can earn salaries approaching or exceeding $500,000 annually, often including bonuses and stock options. Such compensation typically requires advanced skills in deep learning, large-scale data processing, and proficiency with tools like TensorFlow or PyTorch, along with significant industry experience and a strong track record of impactful projects.

Which 3 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, focusing on developing and refining AI models. Other resilient roles include data scientists, who interpret complex data, and cybersecurity professionals, who protect systems from evolving threats. These jobs require specialized skills and adapt to technological changes, making them more resistant to automation.

What are entry-level AI/ML jobs?

Entry-level AI/ML jobs typically include roles such as Junior Machine Learning Engineer, Data Analyst, or AI Research Assistant. These positions often require foundational knowledge of programming languages like Python, familiarity with machine learning frameworks such as TensorFlow or PyTorch, and a relevant degree or certification. They offer opportunities to gain practical experience in model development, data preprocessing, and deploying AI solutions.

What jobs should I get before getting into machine learning engineer?

Entry-level roles such as data analyst, software developer, or research assistant can provide relevant experience for aspiring machine learning engineers. Gaining skills in programming languages like Python or R, understanding data manipulation, and working with tools like SQL and machine learning frameworks are valuable steps before transitioning into a machine learning engineer role.
Machine Learning Engineer Starting jobs near you

Machine Learning Engineer

Bespoke Labs

Frankfort, KY • On-site

Full-time

Re-posted 4 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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