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Online Rlhf Jobs (NOW HIRING)

Normal Computing

AI Research Engineer

New York, NY · On-site

$300K - $400K/yr

Stay current on LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, and program synthesis. What Makes You a Great Fit * PhD in CS/AI/ML (or equivalent research experience) with ...

Normal Computing

AI Engineer

New York, NY · On-site

$200K - $300K/yr

Research or applied experience with LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, or program synthesis * Open-source contributions or publications in AI/ML venues * Skill in ...

DigitalOcean

Staff Software Engineer, AI/ML

Seattle, WA · Hybrid

Experience with agent evaluation, offline/online experiments, and human feedback loops in production. * Direct experience with RLHF, RLAIF, DPO, PPO, GRPO, or related optimization techniques. * Prior ...

Datawizz

Head of Research

San Francisco, CA · Hybrid

$50K - $500K/yr

Design and run rigorous experiments (ablations, offline/online A/Bs), defining clear metrics for ... Develop novel methods (e.g., mixture-of-experts/routing, DPO/RLHF, quantization, speculative ...

Sciforium

LLM Training Engineer

San Francisco, CA · On-site

$155K - $220K/yr

Design offline + online environments that support RL-style training at scale * Instrument ... Post-training pipelines (SFT, RLHF/RLAIF, preference optimization, eval loops) * Building RL ...

DigitalOcean

Staff Software Engineer, AI/ML

Seattle, WA · On-site

Experience with agent evaluation, offline/online experiments, and human feedback loops in production. * Direct experience with RLHF, RLAIF, DPO, PPO, GRPO, or related optimization techniques. * Prior ...

Zillow

Principal Applied Scientist, Agentic AI

$181K - $290K/yr

... RLHF, RLAIF, or DPO for multi-objective optimization. * Develop reward models and objective ... online and batch adaptation loops with strong guardrails. * Translate conversational logs ...

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Online Rlhf information

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$17.5K

$40.6K

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How much do online rlhf jobs pay per year?

As of Jul 11, 2026, the average yearly pay for online rlhf in the United States is $40,596.00, according to ZipRecruiter salary data. Most workers in this role earn between $25,000.00 and $43,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Online RLHF (Reinforcement Learning from Human Feedback) specialists when collaborating with cross-functional teams?

Online RLHF specialists often work closely with machine learning engineers, data annotators, and product managers. A common challenge is ensuring that feedback from human annotators is accurately integrated into model training, which requires clear communication and well-defined annotation guidelines. Additionally, balancing the pace of model updates with the need for high-quality human feedback can be demanding. Effective collaboration and regular syncs are essential to maintain alignment and achieve project goals.

What is the difference between Online Rlhf vs Online Rlhf?

AspectOnline RlhfOnline Rlhf
CredentialsTypically requires certification in online health coaching or related fieldsTypically requires certification in online health coaching or related fields
Work EnvironmentRemote, online platform-basedRemote, online platform-based
Industry UsageCommon in health and wellness sectorsCommon in health and wellness sectors
Job FocusProviding health guidance and support onlineProviding health guidance and support online

Online Rlhf and Online Rlhf are the same role, often used interchangeably. Both involve providing health and wellness support remotely, requiring similar certifications and working within the online health industry. The key difference is often in terminology rather than job function.

What are Online RLHF jobs?

Online RLHF (Reinforcement Learning from Human Feedback) jobs typically involve helping to train AI models by providing human feedback on their outputs. Workers in these roles might review model responses, rate the quality of generated text, or suggest improvements to help the AI learn to produce better results. These jobs are often remote and can be done part-time or as contract work. They play a crucial role in improving the safety, usefulness, and accuracy of AI systems by aligning them more closely with human preferences.

What are the key skills and qualifications needed to thrive as an Online RLHF (Reinforcement Learning from Human Feedback) Specialist, and why are they important?

To thrive as an Online RLHF Specialist, you need a strong background in machine learning, reinforcement learning, and data analysis, typically supported by a degree in computer science or a related field. Familiarity with technical tools like Python, PyTorch or TensorFlow, and experience with human feedback systems or annotation platforms are highly valuable. Strong problem-solving, attention to detail, and the ability to communicate complex concepts clearly are crucial soft skills. These qualifications ensure the effective training and evaluation of AI models, leading to more accurate and reliable machine learning systems.
More about Online Rlhf jobs
What cities are hiring for Online Rlhf jobs? Cities with the most Online Rlhf job openings:
What are the most commonly searched types of Rlhf jobs? The most popular types of Rlhf jobs are:
What states have the most Online Rlhf jobs? States with the most job openings for Online Rlhf jobs include:
What job categories do people searching Online Rlhf jobs look for? The top searched job categories for Online Rlhf jobs are:
Online Rlhf jobs near you
Infographic showing various Online Rlhf job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 1% As Needed, 61% Full Time, 35% Part Time, 1% Temporary, and 1% Contract. Highlights an 81% Physical, 1% Hybrid, and 18% Remote job distribution, with an average salary of $40,596 per year, or $19.5 per hour.

AI Research Engineer

Normal Computing

New York, NY • On-site

$300K - $400K/yr

Full-time

Re-posted 6 days ago

Job description

About Normal Computing
Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.
We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.
Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.
The Role
As an AI Research Engineer at Normal, you will push the frontier of agentic LLMs and reinforcement learning for our agentic code generation tool. You'll design and run experiments, build agents, curate datasets from complex technical documents such as chip specifications, and create rigorous evaluations. You'll write production-quality research code and work closely with engineering to ship improvements to customers. Leadership is not required here; impact through research and building is.
What You'll Own
  • Agent & RL Research: Design and implement multi-agent and RL approaches for agentic code generation and tool use.
  • Research-to-Product: Build research prototypes that integrate with our platform; collaborate to productionize wins.
  • Evaluation: Create evaluation suites: task specs, pass/fail checkers, coverage, cost/latency dashboards.
  • Data Curation: Acquire and curate datasets from PDFs, logs, and tables; generate synthetic data where appropriate; maintain data cards and licensing.
  • Experimental Rigor: Analyze experiments with disciplined ablations; document results and decisions.
  • Field Awareness: Stay current on LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, and program synthesis.

What Makes You a Great Fit
  • PhD in CS/AI/ML (or equivalent research experience) with publications ideally in multi-agent RL, agentic AI, or RL for language/code
  • Strong Python and ML framework experience (PyTorch preferred; JAX/HF a plus)
  • Demonstrated ability to turn research into working systems; reproducibility mindset (tests, seeds, configs, logging)
  • Experience designing eval harnesses and success metrics for sequential/agentic tasks
  • Comfortable with data acquisition and curation from documents and logs; good instincts about data quality and licenses

Bonus Points
  • Research on program synthesis/codegen, constrained decoding, or execution-based rewards
  • Experience with offline RL from tool traces or human corrections
  • Open-source contributions (e.g., CleanRL, RLlib, AutoGen, LangGraph, CrewAI, Transformers)
  • Familiarity with semiconductor/chip domains or other complex technical specs
  • Track record of shipping research to production and measuring impact

Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.

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