Applied ML Engineer - AI/ML Evaluation & Simulation

Job Description:

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.
As an Applied ML Engineer on our team, you'll help develop simulation systems, support data tooling, and contribute to evaluation workflows that improve the reliability of modern AI. You'll collaborate closely with experienced engineers and researchers, learning how to instrument, monitor, and analyze model behavior - especially for language models and agent-style systems. This is a great opportunity for someone early in their career to work with cutting-edge AI technologies in a high-impact, supportive environment. You'll gain experience working with large-scale ML systems, learn best practices in applied AI, and grow your skills across engineering, product, and research.
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
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