... RLHF, and preference learning to improve ad relevance and user experience. • Develop user ... internships. • Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow ...
... RLHF, and preference learning to improve ad relevance and user experience. • Develop user ... internships. • Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow ...
Machine Learning Engineer, Next-Generation Recommendation Systems
New York, NY · On-site +1
$127K - $191K/yr
... RLHF, and preference learning to improve ad relevance and user experience. * Develop user ... Industry experience in ads, recommendation, or user understanding systems (internship experience ...
Machine Learning Engineer, Next-Generation Recommendation Systems
New York, NY · On-site +1
$127K - $191K/yr
... RLHF, and preference learning to improve ad relevance and user experience. * Develop user ... Industry experience in ads, recommendation, or user understanding systems (internship experience ...
... interns, and fostering academic collaborations. We are seeking an early-career researcher who can ... RLVR, RLHF, offline or online, policy- or value-based), and possibly also including reasoning ...
... interns, and fostering academic collaborations. We are seeking an early-career researcher who can ... RLVR, RLHF, offline or online, policy- or value-based), and possibly also including reasoning ...
Research Scientist 4 - Machine Learning and Inference Research, LLM Post-Training
New York, NY · On-site
... interns, and fostering academic collaborations. We are seeking an early-career researcher who can ... RLVR, RLHF, offline or online, policy- or value-based), and possibly also including reasoning ...
Research Scientist 4 - Machine Learning and Inference Research, LLM Post-Training
New York, NY · On-site
... interns, and fostering academic collaborations. We are seeking an early-career researcher who can ... RLVR, RLHF, offline or online, policy- or value-based), and possibly also including reasoning ...
Research Scientist 4 - Machine Learning and Inference Research, LLM Post-Training
New York, NY · On-site
... interns, and fostering academic collaborations. We are seeking an early-career researcher who can ... RLVR, RLHF, offline or online, policy- or value-based), and possibly also including reasoning ...
Research Scientist 4 - Machine Learning and Inference Research, LLM Post-Training
New York, NY · On-site
... interns, and fostering academic collaborations. We are seeking an early-career researcher who can ... RLVR, RLHF, offline or online, policy- or value-based), and possibly also including reasoning ...
Internship Rlhf information
What are Internship RLHF positions?
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 is the difference between Internship Rlhf vs Research Assistant?
| Aspect | Internship Rlhf | Research Assistant |
|---|---|---|
| Required Credentials | Typically enrolled students or recent graduates | Usually requires a relevant degree or ongoing education in the field |
| Work Environment | Internship programs, often in academic or research institutions | Research labs, universities, or research-focused organizations |
| Employer & Industry Usage | Used by educational institutions and research organizations for training | Common in academia, government, and private research sectors |
| Search & Comparison Intent | People comparing internship opportunities or entry-level research roles | Individuals 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 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 (Reinforcement Learning from Human Feedback) 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 are the most commonly searched types of Rlhf jobs in New York? The most popular types of Rlhf jobs in New York are:
What are popular job titles related to Internship Rlhf jobs in New York? For Internship Rlhf jobs in New York, the most frequently searched job titles are:
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What job categories do people searching Internship Rlhf jobs in New York look for? The top searched job categories for Internship Rlhf jobs in New York are:
What cities in New York are hiring for Internship Rlhf jobs? Cities in New York with the most Internship Rlhf job openings:
Full-time
Posted 13 days ago
Job description
Job Summary:
Unity's Vector AI team builds machine learning systems for ad targeting across billions of users. They are seeking a Machine Learning Engineer to develop next-generation recommendation systems that leverage advanced techniques such as reinforcement learning and large language models.
Responsibilities:
• Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.
• Develop user understanding systems — conversion prediction, behavioral modeling, and value estimation — that operate across billions of impressions.
• Apply reinforcement learning and optimization techniques to bidding strategy, auction dynamics, and real-time ad delivery.
• Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation frameworks to measure and improve model quality.
• Partner with engineering to bring research ideas into production, working across the full pipeline from training data to deployed model.
• Communicate findings clearly to technical and non-technical stakeholders across engineering, product, and business teams.
Qualifications:
Required:
• PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or recent graduate).
• Strong research foundations in one or more of: recommendation systems, reinforcement learning, LLM post-training or alignment, human-AI collaboration, probabilistic modeling, or optimization.
• Experience working with large-scale data and ML systems, whether through research or industry internships.
• Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• A track record of rigorous, high-quality research — publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signal.
• Strong written and verbal communication skills — able to make complex ideas accessible across technical and non-technical audiences.
Preferred:
• Industry experience in ads, recommendation, or user understanding systems (internship experience counts).
• Hands-on experience with production ML pipelines — training at scale, feature engineering, or experimentation infrastructure.
• Experience applying LLMs or generative models to ranking, retrieval, or structured prediction problems.
• Familiarity with agentic AI approaches — multi-step reasoning, tool use, or human-AI collaboration frameworks.
• Exposure to causal inference, uplift modeling, or A/B testing at scale.
• Genuine curiosity about applied research and the drive to see ideas through to impact.
Company:
Unity [NYSE: U] offers a suite of tools to create, market, and grow games and interactive experiences across all major platforms from mobile, PC, and console, to extended reality. Founded in 2004, the company is headquartered in San Francisco, USA, with a team of 5001-10000 employees. The company is currently Late Stage.
Unity's Vector AI team builds machine learning systems for ad targeting across billions of users. They are seeking a Machine Learning Engineer to develop next-generation recommendation systems that leverage advanced techniques such as reinforcement learning and large language models.
Responsibilities:
• Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.
• Develop user understanding systems — conversion prediction, behavioral modeling, and value estimation — that operate across billions of impressions.
• Apply reinforcement learning and optimization techniques to bidding strategy, auction dynamics, and real-time ad delivery.
• Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation frameworks to measure and improve model quality.
• Partner with engineering to bring research ideas into production, working across the full pipeline from training data to deployed model.
• Communicate findings clearly to technical and non-technical stakeholders across engineering, product, and business teams.
Qualifications:
Required:
• PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or recent graduate).
• Strong research foundations in one or more of: recommendation systems, reinforcement learning, LLM post-training or alignment, human-AI collaboration, probabilistic modeling, or optimization.
• Experience working with large-scale data and ML systems, whether through research or industry internships.
• Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• A track record of rigorous, high-quality research — publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signal.
• Strong written and verbal communication skills — able to make complex ideas accessible across technical and non-technical audiences.
Preferred:
• Industry experience in ads, recommendation, or user understanding systems (internship experience counts).
• Hands-on experience with production ML pipelines — training at scale, feature engineering, or experimentation infrastructure.
• Experience applying LLMs or generative models to ranking, retrieval, or structured prediction problems.
• Familiarity with agentic AI approaches — multi-step reasoning, tool use, or human-AI collaboration frameworks.
• Exposure to causal inference, uplift modeling, or A/B testing at scale.
• Genuine curiosity about applied research and the drive to see ideas through to impact.
Company:
Unity [NYSE: U] offers a suite of tools to create, market, and grow games and interactive experiences across all major platforms from mobile, PC, and console, to extended reality. Founded in 2004, the company is headquartered in San Francisco, USA, with a team of 5001-10000 employees. The company is currently Late Stage.