ZipRecruiter

Log In

1

Reinforcement Learning With Human Feedback Jobs in Michigan

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

... with real data to enhance operational efficiency. Responsibilities : • Run reinforcement learning experiments in our physically realistic simulators of mineral processing operations, and help turn ...

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $180K/yr

Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus. * Proficiency in Python and comfort reading and ...

Stefanini

AI Engineer

Dearborn, MI · On-site

... automation, reinforcement learning, virtual assistants and specialized programming ... Experience deploying agentic systems with human-in-the-loop or multi-checkpoint validation ...

OneMagnify

Learning & Development Manager

Detroit, MI · On-site

This role partners closely with leadership and HR Partners to identify development needs, build ... Track program effectiveness using metrics, feedback, and performance outcomes Define Career Pathing ...

next page

Showing results 1-20

All JobsReinforcement Learning With Human Feedback JobsReinforcement Learning With Human Feedback Jobs in Michigan

Reinforcement Learning With Human Feedback information

What is reinforcement learning with human feedback?

Reinforcement Learning with Human Feedback (RLHF) is a machine learning technique where AI agents are trained not only through automated reward signals but also by incorporating feedback from humans. This approach helps align the agent’s behavior with human preferences, values, or safety requirements by allowing humans to guide or correct the learning process. RLHF is commonly used in developing advanced AI systems, such as language models, to ensure their outputs are helpful, safe, and aligned with user expectations. The process often involves human evaluators ranking or scoring the AI's responses, which are then used to fine-tune the model’s behavior.

What collaborations are typical for a reinforcement learning with human feedback specialist within a machine learning team?

As an RLHF specialist, you often work closely with data scientists, machine learning engineers, and domain experts to design effective feedback mechanisms and reward models. Collaboration with annotation teams or subject matter experts is common, as high-quality human feedback is crucial for training robust RLHF models. You may also partner with product managers and UX researchers to ensure that the models align with user needs and ethical considerations. Regular cross-functional meetings and code reviews help maintain alignment and foster innovation across teams.

What are the key skills and qualifications needed to thrive as a reinforcement learning with human feedback engineer?

To excel as a Reinforcement Learning with Human Feedback (RLHF) Engineer, you need a strong background in machine learning, reinforcement learning theory, statistics, and typically an advanced degree in computer science or a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like Ray RLlib), and experience with data collection and annotation systems are essential. Excellent problem-solving abilities, communication skills, and teamwork help you collaborate with researchers, data annotators, and other engineers. These skills enable you to design and implement RLHF systems that are robust, scalable, and aligned with human values.

What is the difference between Reinforcement Learning With Human Feedback vs Reinforcement Learning Engineer?

AspectReinforcement Learning With Human FeedbackReinforcement Learning Engineer
CredentialsTypically requires knowledge of machine learning, AI, and data analysisRequires similar credentials in machine learning, programming, and AI
Work EnvironmentResearch labs, AI development teams, tech companiesDevelopment teams, research labs, tech firms
Industry UsageUsed in AI training, human-in-the-loop systems, and model refinementDesigning, implementing, and optimizing reinforcement learning algorithms

Reinforcement Learning With Human Feedback focuses on improving AI models through human input, while Reinforcement Learning Engineers develop and deploy these algorithms. Both roles require strong machine learning skills and often work in similar environments, but their core responsibilities differ in application and focus.

What are popular job titles related to Reinforcement Learning With Human Feedback jobs in Michigan?

For Reinforcement Learning With Human Feedback jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning With Human Feedback jobs in Michigan look for?

The top searched job categories for Reinforcement Learning With Human Feedback jobs in Michigan are:

What cities in Michigan are hiring for Reinforcement Learning With Human Feedback jobs?

Cities in Michigan with the most Reinforcement Learning With Human Feedback job openings:

Reinforcement Learning With Human Feedback jobs near you
Infographic showing various Reinforcement Learning With Human Feedback job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

Ann Arbor, MI • On-site

Mariana Minerals
See more jobs at Mariana Mineralsexternal

Full-time

Re-posted 22 days ago

Job description

Job Summary:
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying critical minerals for modern energy and technology. They are seeking a Machine Learning Engineer to develop and improve machine learning systems for mineral refining facilities, working with real data to enhance operational efficiency.
Responsibilities:
• Run reinforcement learning experiments in our physically realistic simulators of mineral processing operations, and help turn the results into better controllers.
• Build and refine pieces of our training environments—reward functions, observations, and action logic—with guidance from senior engineers.
• Train control models, track and interpret their performance, and dig into why a model underperforms.
• Help close the gap between simulation and reality by comparing model behavior against real plant data and flagging where the physics diverges.
• Write clean, well-tested code and contribute to the services that put models into production.
• Partner with process and chemistry experts to understand the unit operations you're modeling.
Qualifications:
Required:
• 0–4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing—or a strong recent graduate with demonstrated project depth.
• Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
• Proficiency in Python and comfort reading and debugging an existing codebase.
• Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering from experts who will challenge your assumptions.
• A self-starter who asks good questions, ships, and escalates blockers early.
Company:
Mariana Minerals develops mineral projects using technology to supply critical minerals for energy, AI, and defense applications. Founded in 2022, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

Apply