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Tech Stack
Tools & technologiesDockerPythonSQL
About the role
Key responsibilities & impact- Experiment & Build: Design, set up, and execute experiments focusing on LLMs, Agentic AI, model fine-tuning, and broader ML systems.
- Algorithmic Debugging: Actively investigate why a model isn’t converging, why outputs are inconsistent, or why results are unreliable across different ML architectures.
- Code Quality: Write clean, modular code (effective use of classes, functions, and documentation) following strict software engineering best practices.
- Autonomous Delivery: Deliver projects with a high degree of autonomy.
Requirements
What you’ll need- A Master's degree (required) in a highly technical and quantitative field such as Mathematics, Physics, Engineering, Computer Science, or a rigorous double degree.
- You have zero gaps in your machine learning fundamentals.
- Extensive, highly proficient experience with Python and SQL.
- Strong track record working with Language Models, Agentic AI workflows, and deep expertise in model fine-tuning (experience with Knowledge Graphs is a plus).
- Highly capable of deploying solutions into production environments using tools like Docker and other containerization technologies.
- You are exceptionally smart, highly autonomous, and thrive in ambiguity.
- You can present complex research results clearly to both highly technical peers and non-technical business stakeholders.
Benefits
Comp & perks- Flexible work arrangements
- Professional development opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonSQLmodel fine-tuningLanguage ModelsAgentic AIalgorithmic debuggingclean codemodular codesoftware engineering best practicesmachine learning fundamentals
Soft Skills
autonomyproblem-solvingcommunicationpresentation skillsadaptabilitycritical thinkingcollaborationambiguity managementresearch analysisstakeholder engagement
Certifications
Master's degree
