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Contract Machine Learning Engineer Jobs in Indiana

* 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 ...

* 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 ...

* 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 ...

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 ...

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 ...

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 ...

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

Showing results 21-40

Contract Machine Learning Engineer information

See Indiana salary details

$30K

$122.5K

$184.1K

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

As of Sep 9, 2026, the average yearly pay for contract machine learning engineer in Indiana is $122,532.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,600.00 and $147,500.00 per year, depending on experience, location, and employer.

What is a contract machine learning engineer?

A Contract Machine Learning Engineer is a professional who builds and deploys machine learning models on a temporary or project-based basis. They typically work with companies seeking specialized expertise in data science, model development, or AI integration without committing to a full-time hire. Responsibilities may include data preprocessing, model training, algorithm optimization, and deployment. Contract roles allow for flexibility and are often remote, making them ideal for businesses with short-term AI needs or startups looking to scale their machine learning capabilities quickly.

What are the typical day-to-day responsibilities of a contract machine learning engineer?

As a Contract Machine Learning Engineer, your daily tasks usually involve gathering and preprocessing data, building and fine-tuning machine learning models, and collaborating with software engineers and product managers to integrate your models into production systems. You may also meet with clients or internal teams to gather requirements and provide technical insights, as well as document and present your findings to stakeholders. Work is typically project-based and may require a high degree of independence, flexibility, and adaptability. This dynamic environment often exposes you to a variety of industries and technical challenges, making each project unique and providing valuable experience for professional growth.

What are the key skills and qualifications needed to thrive in the contract machine learning engineer position, and why are they important?

To thrive as a Contract Machine Learning Engineer, you need a strong background in machine learning algorithms, data preprocessing, statistical analysis, and proficiency in programming languages such as Python or R, often supported by a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience using cloud platforms (AWS, Google Cloud, Azure) or certifications in these areas are common requirements. Excellent problem-solving, communication, and time management skills are vital, especially when working with cross-functional teams and managing multiple projects remotely. These skills ensure effective delivery of high-quality, scalable machine learning solutions within tight project timelines and diverse client environments.

What are the most commonly searched types of Machine Learning Engineer jobs in Indiana?

The most popular types of Machine Learning Engineer jobs in Indiana are:

What cities in Indiana are hiring for Contract Machine Learning Engineer jobs?

Cities in Indiana with the most Contract Machine Learning Engineer job openings:

Machine Learning Engineer

Terre Haute, IN โ€ข On-site

Full-time

Re-posted 23 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