Senior ML Engineer (AI Research)

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.
  • Track record of building and delivering products or technical systems in a dynamic, startup-like environment is a plus.
  • Experience engineering complex systems such as large-scale distributed data processing platforms or high-load web services is advantageous.
  • Open-source projects demonstrating strong engineering capabilities are a plus.
  • Excellent command of English, with strong technical writing, articulation, and communication skills.
  • Proficiency with contemporary software engineering practices, including CI/CD, version control, and unit testing.
  • Ability to work independently while collaborating effectively with multidisciplinary research and engineering teams.
  • Strong curiosity, analytical thinking, and enthusiasm for solving open-ended and technically challenging AI problems.

  Benefits:

  • Competitive compensation.
  • Career growth and continuous learning opportunities.
  • Flexibility and significant ownership in your work.
  • Collaborative and innovative research and engineering culture.
  • Opportunity to work on impactful AI research and development projects.
  • Exposure to advanced machine learning, reinforcement learning, LLMs, and agentic AI systems.
  • Opportunity to contribute to research that can be translated into practical AI applications.
  • International environment with talented research and engineering teams.
  • Inclusive workplace committed to equal employment opportunities.
  • Workplace accommodations available during the application process where required.
  • Employment is subject to authorization to work in the country where the position is based.