Accountabilities

• Conduct applied machine learning research across areas such as guided search, reinforcement learning, agentic systems, reasoning models, and model distillation.
• Design and execute experiments to identify efficient approaches for training large language models using interaction traces from diverse environments.
• Explore methods for guided generation and search within model trajectory spaces.
• Investigate approaches for collecting and mining relevant training data at web scale.
• Develop efficient methods for incorporating large-scale data into model post-training workflows.
• Experiment with different reinforcement learning configurations in domains where rewards can be verified.
• Explore approaches for training AI agents on tasks where reward signals are difficult or impossible to verify directly.
• Formulate research questions and translate hypotheses into well-designed machine learning experiments.
• Design experiments with appropriate statistical rigor, ensuring results are reliable, interpretable, and reproducible.
• Analyze experimental results and identify meaningful conclusions, limitations, and opportunities for further research.
• Develop and train large-scale machine learning models across multiple computational nodes.
• Implement research ideas using modern deep learning frameworks, primarily Python and JAX.
• Translate promising research findings into practical solutions in collaboration with adjacent engineering and research teams.
• Contribute to technical publications, research reports, and clear documentation of experimental findings.
• Help shape research directions by identifying promising techniques, evaluating alternatives, and communicating results to technical stakeholders.
• Collaborate with engineers and researchers to develop scalable systems for experimentation, training, evaluation, and model improvement.
• Provide technical leadership and mentorship while contributing hands-on engineering expertise to complex AI research initiatives.
• Contribute to engineering practices that support reliable and reproducible research, including testing, version control, and continuous integration.
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Requirements

• Significant professional experience in machine learning engineering, applied AI research, or a closely related field, at senior or staff level.
• Profound understanding of the theoretical foundations of machine learning and reinforcement learning.
• Deep expertise in modern deep learning for language processing and generation.
• Substantial experience training large-scale models across multiple computational nodes.
• Strong software engineering capabilities, particularly with Python.
• Deep hands-on experience with modern deep learning frameworks, particularly JAX.
• Strong experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor.
• Ability to formulate meaningful research questions, design experiments to test hypotheses, and derive actionable conclusions from results.
• Strong understanding of experimental methodology, model evaluation, and interpretation of machine learning results.
• Excellent ability to document technical and research findings clearly and contribute to publications or detailed technical reports.
• Strong communication, collaboration, and technical leadership skills.
• Experience with deep reinforcement learning for LLMs, including techniques such as reward modeling, DPO, and PPO, is an advantage.
• Familiarity with important modern LLM concepts and techniques such as RoPE, ZeRO/FSDP, Flash Attention, and quantization is beneficial.
• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline; a Master’s degree or PhD is preferred.

Accountabilities

• Conduct applied machine learning research across areas such as guided search, reinforcement learning, agentic systems, reasoning models, and model distillation.
• Design and execute experiments to identify efficient approaches for training large language models using interaction traces from diverse environments.
• Explore methods for guided generation and search within model trajectory spaces.
• Investigate approaches for collecting and mining relevant training data at web scale.
• Develop efficient methods for incorporating large-scale data into model post-training workflows.
• Experiment with different reinforcement learning configurations in domains where rewards can be verified.
• Explore approaches for training AI agents on tasks where reward signals are difficult or impossible to verify directly.
• Formulate research questions and translate hypotheses into well-designed machine learning experiments.
• Design experiments with appropriate statistical rigor, ensuring results are reliable, interpretable, and reproducible.
• Analyze experimental results and identify meaningful conclusions, limitations, and opportunities for further research.
• Develop and train large-scale machine learning models across multiple computational nodes.
• Implement research ideas using modern deep learning frameworks, primarily Python and JAX.
• Translate promising research findings into practical solutions in collaboration with adjacent engineering and research teams.
• Contribute to technical publications, research reports, and clear documentation of experimental findings.
• Help shape research directions by identifying promising techniques, evaluating alternatives, and communicating results to technical stakeholders.
• Collaborate with engineers and researchers to develop scalable systems for experimentation, training, evaluation, and model improvement.
• Provide technical leadership and mentorship while contributing hands-on engineering expertise to complex AI research initiatives.
• Contribute to engineering practices that support reliable and reproducible research, including testing, version control, and continuous integration.
• 
  

Requirements

• Significant professional experience in machine learning engineering, applied AI research, or a closely related field, at senior or staff level.
• Profound understanding of the theoretical foundations of machine learning and reinforcement learning.
• Deep expertise in modern deep learning for language processing and generation.
• Substantial experience training large-scale models across multiple computational nodes.
• Strong software engineering capabilities, particularly with Python.
• Deep hands-on experience with modern deep learning frameworks, particularly JAX.
• Strong experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor.
• Ability to formulate meaningful research questions, design experiments to test hypotheses, and derive actionable conclusions from results.
• Strong understanding of experimental methodology, model evaluation, and interpretation of machine learning results.
• Excellent ability to document technical and research findings clearly and contribute to publications or detailed technical reports.
• Strong communication, collaboration, and technical leadership skills.
• Experience with deep reinforcement learning for LLMs, including techniques such as reward modeling, DPO, and PPO, is an advantage.
• Familiarity with important modern LLM concepts and techniques such as RoPE, ZeRO/FSDP, Flash Attention, and quantization is beneficial.
• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline; a Master’s degree or PhD is preferred.