(Senior) Machine Learning Scientist (Modeling & Evaluation)

We are looking for a Machine Learning Scientist (Modeling & Evaluation) to join the Enterprise Solution Science Team and help enterprises turn AI into real ROI. In this role, you will be responsible for end-to-end development, from problem definition to driving models and solutions into production.

Responsibilities

Design, develop, and deploy optimization solutions built on ML models and evaluation, to improve efficiency and quality; Work with PMs and engineers to turn product goals and customer needs into clear problems and priorities, and integrate validated methods into the product; Build reliable evaluation methods for LLM output to measure the quality and impact of solutions, and decide if the results apply to real-world conditions; Form your own hypotheses, then design A/B tests that use real traffic to validate them; Follow the latest research and industry solutions, evaluate them in our own context, and propose new approaches, deciding what to build in-house and what to adopt; Monitor solutions after launch, and proactively communicate risks, trade-offs, and progress; (Optional) Mentor junior scientists and interns, if the need arises

Requirements

Master's or PhD degree in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or related fields; At least 2 years of experience in machine learning or engineering roles (5+ years preferred); Hands-on experience with ML models (including classification, regression, ranking, and retrieval) and quality evaluation (including LLM-as-a-judge); Solid foundation in statistics and the ability to explain how these techniques relate to business impact; Impact-driven mindset: able to judge task priority, collaborate across functions, and proactively drive work forward and surface risks; AI-native development: work daily with coding agents that read the codebase, run tests, and iterate on their own, while setting the scope and reviewing the diffs; Can explain and correct what the agent produced

Nice to have

Experience leading projects; Ability to form hypotheses and validate them: design experiments and A/B tests, interpret results, and take ownership of conclusions; Background in embeddings and causal inference; Engineering experience with AI / LLM applications (for example, serving and integration)