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Rlhf Ai Jobs (NOW HIRING)

... of AI/ML markets; familiarity with RLHF and its applications is strongly preferred. - Strong network in San Francisco and U.S. tech ecosystem. - Entrepreneurial mindset, with ability to thrive in a ...

RLHF, DPO, or equivalent • Experience designing evaluation frameworks for LLM or agentic systems ... Bronco AI is an intelligent integration layer across your legacy IT so you can get things done ...

AI Engagement Manager

$150K - $180K/yr

About This Role AI Engagement Managers own the relationships with Pareto's most important accounts ... Working knowledge of ML and data workflows: data labeling, evals, RLHF, red-teaming, or adjacent ...

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be ...

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How much do rlhf ai jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for rlhf ai in the United States is $65.77, according to ZipRecruiter salary data. Most workers in this role earn between $45.43 and $100.00 per hour, depending on experience, location, and employer.
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Infographic showing various Rlhf Ai job openings in the United States as of August 2026, with employment types broken down into 75% Full Time, 21% Part Time, and 4% Contract. Highlights an 67% Physical, 3% Hybrid, and 30% Remote job distribution, with an average salary of $136,810 per year, or $65.8 per hour.

Technical Program Manager, Model Alignment and Deployment

Character.AI

Redwood City, CA • On-site

Full-time

Re-posted 7 days ago


Job description

Job Summary:
Character.AI empowers people to connect, learn and tell stories through interactive entertainment. They are seeking a Technical Program Manager to lead cross-functional programs that enhance model alignment and deployment, ensuring the integration of user experience, safety, and research into scalable AI products.
Responsibilities:
• Program ownership: Lead planning and execution of cross-functional programs spanning data collection, annotation pipelines, alignment workflows (RLHF, DPO, Constitutional AI), safety guardrails (adversarial testing, red-teaming), and model serving. Establish scopes, goals, timelines, risks, and success metrics.
• Cross-functional coordination: Serve as the connective tissue between Post-Training, Safety Engineering, Trust & Safety, ML Infra, UXR, and Product. Translate model development, safety, and user experience priorities into executable roadmaps, keeping tightly coupled workstreams aligned from post-training through to production deployment.
• Evaluation & quality: Develop and maintain custom evaluation frameworks to track model performance and user satisfaction. Drive comprehensive quality evaluation initiatives alongside rigorous safety and toxicity baselines. Partner with UXR, researchers, and engineers to identify quality signals, incorporate human feedback, and surface actionable insights on model behavior in production.
• Operational excellence: Drive visibility into data pipeline health, annotation quality, training run progress, and deployment readiness. Identify bottlenecks across teams and lead efforts to improve tooling, process, and developer velocity.
• Strategic partnership: Partner with research, safety, product, and UXR leadership on prioritization, sequencing, and tradeoffs—balancing aggressive capability scaling with strict safety requirements, user needs, and infrastructure constraints.
• Process development: Build and refine the operational patterns, ontologies, and frameworks used to scale new capability development—from prompt engineering and data generation to model behavior specification and safety guidelines.
• Vendor & partner management: Own external partner relationships supporting these workstreams, including general and safety-focused annotation vendors, evaluation tooling providers, and data partners.
Qualifications:
Required:
• 5+ years of experience in technical program management, research operations, or product execution in a fast-moving AI, ML, or research environment.
• Deep familiarity with post-training and alignment concepts (supervised fine-tuning, RLHF, AI safety frameworks, LLM evaluation) as well as model deployment/serving, sufficient to engage substantively with both research and infrastructure engineers.
• Proven ability to lead complex, multi-team programs in ambiguous, rapidly evolving environments; track record of shipping with quality and speed.
• Strong analytical mindset; comfortable working with data and user insights to measure program health, identify trends, and drive decisions.
• Proficiency in SQL and Python.
• Exceptional communication skills - able to translate deep technical work into clear narratives for leadership, and to hold detailed technical conversations with engineers across different disciplines.
• Obsessive about data integrity, operational rigor, and process quality without letting process slow teams down.
• BS in a quantitative, scientific, or technical field; MS or PhD a plus.
Preferred:
• Hands-on experience with data pipelines, annotation platforms, ML evaluation tooling, or human-in-the-loop workflows.
• Experience managing annotation vendors or external data partners.
• Familiarity with distributed training, experiment tracking, or ML infrastructure (Kubernetes, Docker, cloud) and model serving systems.
• Prior experience embedded in an AI research team, foundation model lab, or Trust & Safety engineering team.
• Direct experience managing AI safety, trust, quality eval, or red-teaming programs.
Company:
Character.ai provides open-ended conversational applications in which users create characters and converse with them. Founded in 2021, the company is headquartered in Menlo Park, USA, with a team of 51-200 employees. The company is currently Growth Stage.