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How much do internship rlhf jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for internship rlhf in Bridgewater, NJ is $15.89, according to ZipRecruiter salary data. Most workers in this role earn between $12.79 and $17.93 per hour, depending on experience, location, and employer.

What is an internship RLHF?

Internship RLHF positions refer to internships focused on Reinforcement Learning from Human Feedback (RLHF), a cutting-edge area in artificial intelligence research. Interns in RLHF roles typically work on projects that involve training AI models to align with human preferences using feedback data, often in natural language processing or robotics. These internships are usually offered by tech companies or research labs and provide hands-on experience in machine learning, data analysis, and experimental design. RLHF interns often collaborate with experienced researchers and engineers to advance AI systems' safety, reliability, and alignment with human values.

What types of projects and tasks can I expect to work on during an RLHF internship?

As an RLHF (Reinforcement Learning from Human Feedback) intern, you can expect to engage in a variety of projects that combine machine learning, data annotation, and model evaluation. Typical tasks include curating and labeling datasets, training and fine-tuning machine learning models using human feedback, and conducting experiments to evaluate model performance. You may also collaborate closely with engineers and researchers, participate in team meetings, and contribute to documentation or research publications. This hands-on experience will help you develop both technical and collaborative skills essential for a career in AI research.

What are the key skills and qualifications needed to thrive as an RLHF intern, and why are they important?

To thrive as an RLHF Intern, you need a solid background in machine learning, statistics, and programming (especially Python), usually supported by ongoing or completed studies in computer science or a related field. Experience with deep learning frameworks (such as TensorFlow or PyTorch), version control systems (like Git), and familiarity with reinforcement learning libraries are typically required. Strong problem-solving abilities, curiosity, and effective teamwork and communication skills help interns contribute meaningfully and learn quickly. These skills and qualities are crucial for successfully developing, evaluating, and improving RLHF models in a collaborative research environment.

What is the difference between Internship Rlhf vs Research Assistant?

AspectInternship RlhfResearch Assistant
Required CredentialsTypically enrolled students or recent graduatesUsually requires a relevant degree or ongoing education in the field
Work EnvironmentInternship programs, often in academic or research institutionsResearch labs, universities, or research-focused organizations
Employer & Industry UsageUsed by educational institutions and research organizations for trainingCommon in academia, government, and private research sectors
Search & Comparison IntentPeople comparing internship opportunities or entry-level research rolesIndividuals seeking research support or entry-level research positions

Internship Rlhf and Research Assistant roles both involve research activities, but internships are typically short-term training positions for students or recent graduates, while research assistants are more formal, often requiring relevant education and supporting ongoing research projects. Understanding these differences helps candidates choose the right opportunity based on their experience and career goals.

What job categories do people searching Internship Rlhf jobs in Bridgewater, NJ look for?

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What cities near Bridgewater, NJ are hiring for Internship Rlhf jobs?

Cities near Bridgewater, NJ with the most Internship Rlhf job openings:

Internship Rlhf jobs near you

Machine Learning Engineer, Evals

NOUS RESEARCH

New York, NY • On-site

$153K/yr

Full-time

Re-posted 19 days ago

Key responsibilities

  • Run the full evaluation pipeline end to end and reproduce known results during onboarding.

  • Build a judge calibration protocol to measure agreement and identify drift zones.

  • Extend existing benchmarks with new tasks targeting known capability gaps and perform failure analysis on model outputs.

Job description

You'll work across the lab on agent capability evals, benchmark design, LLM-as-judge systems, failure analysis, and the infrastructure that ties it together. This is a high-growth, high-ownership role on a small team, and you'll ship evaluation infrastructure that researchers depend on from day one.
Responsibilities:
  • Run the full eval pipeline end to end and reproduce known results during onboarding, pairing with a senior engineer on your first task
  • Build a judge calibration protocol: sample human-labeled decisions, measure agreement (κ, per-class P/R), identify drift zones, and document it so anyone can re-run it
  • Extend an existing benchmark (GAIA, τ-Bench, SWE-bench slice, etc.) with new tasks targeting known capability gaps, including the prompt, environment, rubric, automated grader, and QA
  • Run failure analysis on model outputs: categorize failure modes, quantify prevalence, and write up findings with recommendations for training data, judge prompts, or benchmark changes
  • Own a recurring eval workflow (weekly regression suite, judge drift dashboard, red-team evaluation for a new capability) and ship tooling researchers actually use

Qualifications:
  • 3+ years in software engineering, ML engineering, data science, or a research-adjacent role, with concrete evaluation experience from coursework, an internship, a side project, open source work, or a job
  • Experience with at least one LLM evaluation framework (Harbor, Nemo Evaluator, etc.), with real opinions on what it does well and where it falls short
  • Hands-on experience with LLMs: prompting, few-shot design, and ideally fine-tuning or RAG; regular use of coding agents
  • Solid Python. You write clean, tested, version-controlled code that a colleague could run without you babysitting it
  • Comfort with Git, CI/CD basics, Docker, and the Linux command line (SSH, tmux, debugging a remote job)
  • Understanding of basic eval statistics: why accuracy misleads on imbalanced judges, what Cohen's κ measures, how to think about confidence intervals on a metric
  • At least 3 of the following: you can explain why LLM-as-judge needs calibration; you've done failure analysis and can tell model bugs apart from prompt, grader, or retrieval issues; you know at least two agent benchmarks (GAIA, AgentBench, τ-Bench, MINT, SWE-bench, WebShop, ALFWorld) and a limitation of each; you've designed or extended an eval dataset with happy paths, edge cases, and adversarial examples; you've thought about non-determinism in eval, how you sample, how many runs, how you report variance
  • You communicate clearly to both researchers and engineers, in the right language for each
  • You're comfortable with ambiguity, can turn a half-formed request into a plan, and know when to ask for help

Preferred:
  • RLVR / RLHF pipeline experience
  • Training data curation experience
  • Distributed eval orchestration experience
  • Benchmark design from scratch
  • Red teaming and adversarial eval experience
  • Familiarity with psychometrics or measurement theory

HOW TO APPLY
Email recruiting@nousresearch.com with the following information:
  • In the Subject: The role you are applying for
  • Resume/CV
  • Cover Letter
  • A portfolio showcasing your work (e.g., GitHub Account)

Apply