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Online Rlhf Jobs (NOW HIRING)

Apply Reinforcement Learning (RLVR, RLHF), Direct Preference Optimization (DPO), and customer ... well as online experiments. About the team Core Search builds the next-generation LLM-powered ...

Apply Reinforcement Learning (RLVR, RLHF), Direct Preference Optimization (DPO), and customer ... well as online experiments. About the team Core Search builds the next-generation LLM-powered ...

Here on the Apple Store Online team, we are responsible for Apple's largest store. Our main goal is ... Experience designing RLHF or structured human feedback programs.Background in large language model ...

Here on the Apple Store Online team, we are responsible for Apple's largest store. Our main goal is ... Preferred Qualifications Experience designing RLHF or structured human feedback programs.

Senior AI Engineer

Los Angeles, CA

$112K - $154K/yr

Own requirements data prep feature engineering classical ML or LLM fine-tuning (LoRA, PEFT, RLHF) offline/online evaluation MLflow registry, with automated drift and quality alerts. * Data & Storage ...

Design experiments, define success metrics, and run rigorous offline and online evaluations (A/B ... Familiarity with LLM fine-tuning techniques (LoRA, RLHF, instruction tuning) and serving ...

Senior AI Engineer

Los Angeles, CA

$112K - $154K/yr

End-to-End ML Lifecycle:  Own requirements → data prep → feature engineering → classical ML or LLM fine-tuning (LoRA, PEFT, RLHF) → offline/online evaluation → MLflow registry, with ...

Design experiments, define success metrics, and run rigorous offline and online evaluations (A/B ... Familiarity with LLM fine-tuning techniques (LoRA, RLHF, instruction tuning) and serving ...

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Online Rlhf information

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$17.5K

$40.6K

$86K

How much do online rlhf jobs pay per year?

As of Jun 21, 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:
Machine Learning Engineer - Reinforcement Learning

Machine Learning Engineer - Reinforcement Learning

Pony.ai

Fremont, CA • On-site

Full-time

Posted 10 days ago


Job description

Job Summary:
Pony.ai is a global leader in autonomous mobility, recognized for its innovative technologies and services in the field. The role involves building scalable systems for training large generative models, implementing reinforcement learning methods, and shipping deep learning solutions to enhance self-driving behaviors.
Responsibilities:
• Build scalable systems for training and fine-tuning large generative models that produce realistic, informative driving behaviors for evaluation and scenario coverage.
• Implement and iterate on RL-style methods: algorithms, reward / preference objectives, and training setups suited to high-fidelity, insightful behaviors in simulation-aligned workflows (closed-loop evaluation mindset).
• Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led triaging, automate high-volume workflows, and support nuanced analysis of self-driving behavior to surface critical anomalies.
• Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and iteration of models used to judge performance across large real-world exposure.
• Design and evolve data + evaluation systems inspired by RL from human preferences (RLHF) and related paradigms—turning preference/judgment signals into repeatable, scalable training and evaluation loops.
• Partner broadly with teams such as Prediction, Planning, Research, and platform/engineering leads to land cross-cutting improvements with clear metrics.
Qualifications:
Required:
• M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field—or equivalent practical experience.
• Hands-on experience building and applying ML in production-grade settings, with a strong RL component (policy learning, preference/feedback optimization, or offline/online RL pipelines).
• Depth in deep learning, sequence modeling, and generative models.
• Demonstrated impact via strong publications or a clear history of shipping impactful ML systems end-to-end.
• Experience with large-scale distributed training and large-scale data processing.
• Ability to lead ambiguous technical work from problem framing through reliable delivery.
Preferred:
• Background in autonomous vehicles, robotics, or complex simulation environments.
• Strong grasp of modern RL and post-training techniques in LLM, dLLM, VLA and video generations.
• Hands-on integration of simulation platforms with ML training and evaluation workflows.
• Python fluency and frameworks such as PyTorch.
• Experience defining and operating metrics for complex, safety-critical AI systems.
• Technical leadership: influencing stakeholders, aligning teams, and raising the bar for evaluation rigor.
• Excellent communication—simple explanations of complex trade-offs.
Company:
Pony.ai develops autonomous driving technology for vehicles that operates using artificial intelligence and machine learning. Founded in 2016, the company is headquartered in Fremont, USA, with a team of 1001-5000 employees. The company is currently Late Stage.