AI​/Machine Learning Engineer

About the Role

We are looking for an Evaluation Scientist who can work across both hands-on experimentation and automation infrastructure
. This role begins with running manual evaluations (e.g., executing and monitoring individual experiments) and progresses toward building scripts, tools, and infrastructure that streamline and automate these processes, with the long-term goal of reducing manual work as much as possible.

The ideal candidate will also bring expertise in coding agents and quality evaluation
, enabling them to design robust experiments and improve workflows. While the role will receive high-level guidance, candidates should be able to independently define and implement the lower-level details of experiment setup after ramping up. For example, given a high-level requirement for a new type of evaluation, the candidate should be able to propose and execute an implementation plan with detailed steps, metrics, and automation in place.

Key Responsibilities

• Run and manage manual evaluation experiments across AI/ML systems.
• Develop and maintain automation infrastructure (scripts, pipelines, tools) to reduce manual evaluation work.
• Design and execute new types of evaluations
  , translating broad research questions into structured experiment setups.
• Work with coding agents and applied ML workflows to define and measure quality.
• Define metrics, benchmarks, and evaluation criteria to assess performance and identify gaps.
• Collaborate with research leads to align evaluation design with project goals while owning implementation details
  .
• Ensure reproducibility, consistency, and scalability of evaluation processes.

Qualifications

• Strong coding skills in Python (or equivalent) for scripting, automation, and experiment design.
• Experience with running and analyzing experiments
  , including quality evaluation methodologies.
• Knowledge of coding agents, ML models, or applied automation frameworks
  .
• Ability to work independently
  : take high-level requirements and define detailed steps for execution.
• 2–4 years of hands-on experience in evaluation, scripting, or applied data science/ML (academic or industry).
• Strong analytical skills with experience in data handling, reporting, and experiment analysis
  .

Preferred Skills

• Familiarity with evaluation frameworks and automation tools in AI/ML research.
• Experience in building scalable infrastructure for experiments or evaluations.
• Knowledge of experimental design, statistical testing, or quality benchmarking
  .