... reinforcement learning (MDPs, Bellman equations, dynamic programming) * Hands-on experience with ... Utilize feedback from mid-quarter and end-of-quarter evaluations to improve teaching * Identify ...
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... reinforcement learning (MDPs, Bellman equations, dynamic programming) * Hands-on experience with ... Utilize feedback from mid-quarter and end-of-quarter evaluations to improve teaching * Identify ...
... reinforcement learning (MDPs, Bellman equations, dynamic programming) * Hands-on experience with ... Utilize feedback from mid-quarter and end-of-quarter evaluations to improve teaching * Identify ...
... reinforcement learning (MDPs, Bellman equations, dynamic programming) * Hands-on experience with ... Utilize feedback from mid-quarter and end-of-quarter evaluations to improve teaching * Identify ...
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Salt Lake City, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Salt Lake City, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Logan, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Logan, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Ogden, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Ogden, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
West Jordan, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
West Jordan, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Orem, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Orem, UT · Remote
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Logan, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Logan, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
West Jordan, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
West Jordan, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Ogden, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Ogden, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Salt Lake City, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Salt Lake City, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Orem, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Orem, UT · On-site
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Reinforcement learning from customer and product feedback * Reward modeling and preference learning ... Experience with reinforcement learning, reward modeling, or sequential decision-making.
New
Lead Decision Intelligence Engineer - NBA
Salt Lake City, UT · On-site
$129 - $178/hr
Key Responsibilities Reinforcement Learning Model Development * Design, implement, and evaluate RL ... Collaborate with platform architects to define feedback loop contracts: how disposition outcomes ...
Lead Decision Intelligence Engineer - NBA
Salt Lake City, UT · On-site
$129 - $178/hr
Key Responsibilities Reinforcement Learning Model Development * Design, implement, and evaluate RL ... Collaborate with platform architects to define feedback loop contracts: how disposition outcomes ...
Senior Machine Learning Engineer
Salt Lake City, UT · On-site
$101K - $138K/yr
... with research staff and technical staff. * Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability ...
Senior Machine Learning Engineer
Salt Lake City, UT · On-site
$101K - $138K/yr
... with research staff and technical staff. * Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability ...
... our Human Resources Team at 801-366-6947 option 1 or email macurecruiting@macu.com and every ... with facilitators and business partners to support effective delivery, adoption, and reinforcement ...
... our Human Resources Team at 801-366-6947 option 1 or email macurecruiting@macu.com and every ... with facilitators and business partners to support effective delivery, adoption, and reinforcement ...
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?
| Aspect | Reinforcement Learning With Human Feedback | Reinforcement Learning Engineer |
|---|---|---|
| Credentials | Typically requires knowledge of machine learning, AI, and data analysis | Requires similar credentials in machine learning, programming, and AI |
| Work Environment | Research labs, AI development teams, tech companies | Development teams, research labs, tech firms |
| Industry Usage | Used in AI training, human-in-the-loop systems, and model refinement | Designing, 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 Utah?
For Reinforcement Learning With Human Feedback jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning With Human Feedback jobs in Utah look for?
The top searched job categories for Reinforcement Learning With Human Feedback jobs in Utah are:
What cities in Utah are hiring for Reinforcement Learning With Human Feedback jobs?
Cities in Utah with the most Reinforcement Learning With Human Feedback job openings:
Adjunct Instructor (Artificial Intelligence & Machine Learning)
Salt Lake City, UT
Full-time
Re-posted 23 hours ago
Job description
As a Neumont University instructor you will develop leadership and mentoring skills that will enhance your career. It can be a very rewarding experience as you see students start to understand complex subjects and gain confidence in their abilities.
Neumont is looking to fill multiple adjunct faculty positions to teach (in-person) in the following area:
- Artificial Intelligence
Neumont University is looking for tech individuals with in-industry experience to teach the following technologies:
- Artificial Intelligence:
- Ability to teach AI fundamentals to complete beginners in clear, accessible terms
- Familiarity with low-code AI tools, including Microsoft Azure ML Studio
- AI Data Modeling:
- Proficiency in SQL/SQLite and relational database design (entity-relationship modeling through normalization)
- Experience with dimensional modeling / data warehousing (star schemas, OLTP vs. OLAP)
- Understanding of how data modeling supports ML pipelines (feature stores, vector stores/embeddings, train/serve skew)
- Ability to teach query optimization, including indexes and reading query execution plans
- Strong working proficiency in Python, sufficient to review and debug student code live in class
- Machine Learning Foundations:
- Comfort teaching and evaluating algorithmic complexity (Big-O: time and space)
- Solid grounding in core ML concepts: supervised/unsupervised/reinforcement learning, overfitting, neural network fundamentals
Reinforcement Learning:
- Solid theoretical grounding in reinforcement learning (MDPs, Bellman equations, dynamic programming)
- Hands-on experience with Monte Carlo methods, TD learning/SARSA, Q-learning, policy gradients, and DQNs
- Working knowledge of NumPy and PyTorch
- Ability to teach on-policy vs. off-policy tradeoffs and deep-RL stability concepts (replay buffers, target networks)
QUALIFICATIONS:
- Bachelor’s or higher degree in computer science or a related field AND 4 years of CS related experience (or 8 years of CS related experience without a CS degree)
- Teaching experience preferred, but not required
- Ability to work within the U.S. without company sponsorship
LOCATION: In-person, on campus
TIME COMMITMENT:
- Courses begin October 5, 2026 and are 5 to 10 weeks long, depending on the course.
- We make classes work around full-time work schedules as we offer AM and PM classes.
- Adjuncts may spend up to 10 hours a week outside of class doing grading and familiarizing themselves with the curriculum. This time commitment lessens once they get the hang of teaching.
ADVANTAGES TO BEING A NEUMONT FACULTY MEMBER:
- Improve the lives of students from across the nation through the power of education.
- Opportunity to give back through educating the next generation of tech experts.
- Experience the "light" turn on in your student's eyes as you teach and they experience true understanding.
- Be a part of a computer science institution that focuses on creating software engineers that can DO, not just theorize.
- Develop your teaching/mentoring skills.
Faculty at Neumont University are responsible for educating students in accordance with the Neumont teaching methodology, which focuses on active learning and engaging students in the learning environment. Faculty members are also responsible for grading and providing valuable feedback to students in a timely manner, mentoring students in groups or individually, evaluating curriculum, adapting coursework and materials as necessary to meet student learning needs, and other activities related to effective instruction.
RESPONSIBILITIES, INCLUDING BUT NOT LIMITED TO:
- Implement best practices in teaching and project-based learning
- Submit all new teaching materials to Neumont vault upon completion of each course
- Work with supervisor to identify areas for personal development and course improvement
- Utilize feedback from mid-quarter and end-of-quarter evaluations to improve teaching
- Identify innovative teaching methods to solve curricular problems
- Teach material defined in the course description and syllabus
- Maintain and meet the listed student learning goals
- Utilize the Neumont LMS to keep an updated syllabus, course materials, and grades
- Provide a safe learning environment for students
- Answer and deal respectfully with student complaints and problems
- Use effective assessments that measure student learning
- Provide timely and accurate feedback to students’ assignments, exams, projects, etc.
FAQ
I’ve never taught before, am I qualified to teach?
We hire industry professionals and help them learn how to be good teachers. Our project-based curriculum means fewer lectures and more hands-on practice. Instructors must have at least 4 years of industry experience and a bachelor’s degree in a tech related field. An additional 4 years of outstanding experience and contributions to the field may be substituted for a formal degree.
Do I have to develop the curriculum?
You will use curriculum that has been developed and refined by previous Neumont instructors. We expect that you will share your individual perspective and experiences with the students to supplement the formal curriculum.
Does Neumont offer online courses?
No. Our classes are in-person as it makes for a better teaching/learning experience.
What is the process to get started?
All instructors will go through a formal application process which includes a short teaching demonstration. We will verify your work experience and educational credentials.
NU is an equal opportunity employer and provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Neumont’s Annual Security & Fire Safety Report is available online at https://www.neumont.edu/campus-safety under the Student Life section. This report is required by federal law to comply with the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and contains policy statements and crime statistics for the school. The policy statements address the school’s policies, procedures and programs concerning safety and security. You may also request a paper copy from the Vice President, Student Affairs.
About Neumont College of Computer Science
Sourced by ZipRecruiter
Industry
Colleges, universities, and professional schools
Company size
51 - 200 Employees
Headquarters location
Salt Lake City, UT, US
Year founded
2003