About the job
We’re building the next generation of AI evaluation systems — and we’re looking for a motivated early-career engineer who’s excited to work at the intersection of ML, software, and product. You’ll join a team focused on making AI systems — including LLMs and agentic AI - more measurable, testable, and trustworthy in real-world scenarios. This is a hands-on, collaborative role ideal for someone with a strong foundation in software engineering and machine learning, and an eagerness to grow by building tools and systems that help evaluate advanced AI behavior at scale.
Responsibilities
Contribute to systems that simulate interactive behaviors (including LLM-driven agents)
Help build tools to support dataset generation and evaluation workflows
Assist in developing pipelines for structured insights from model behavior
Collaborate with teammates to debug and improve evaluation systems
Write clean, testable code to support scalable and reliable infrastructure
Learn how to define metrics that connect model behavior to real-world outcomes
Qualifications
Minimum
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field
Strong programming skills in Python or another modern language (e.g., Java, Swift, Go)
Basic understanding of machine learning principles
Interest in LLMs, generative AI, or agent-based systems
Curiosity about how to evaluate and improve real-world AI performance
Strong collaboration and communication skills
Preferred
Coursework or internship experience in ML, AI systems, or applied data science
Familiarity with training or evaluating models (even via coursework or personal projects)
Exposure to tools like PyTorch, TensorFlow, or Hugging Face
Interest in AI observability, behavior simulation, or synthetic data
Passion for working cross-functionally in fast-moving, exploratory teams