About The Role

ABOUT THE ROLE

This is a founding-level ML research engineering role at a fast-moving AI data and evaluation company serving top frontier AI labs. You'll work directly with the founding team to build and scale the AI research backbone from the ground up, sitting at the intersection of applied machine learning and systems engineering.

WHAT YOU'LL DO

Design, train, and evaluate ML models, including LLMs, diffusion models, and domain-specific architectures.
Develop scalable experimentation pipelines spanning data, model training, and evaluation workflows.
Collaborate with data and infrastructure teams to optimize training throughput and data quality.
Contribute to open research, internal benchmarks, and emerging techniques in multimodal and generative AI.
Rapidly prototype research insights and productionize them into usable tools and models.
Help define foundational technical culture by setting standards for research rigor, documentation, and reproducibility.

WHAT WE'RE LOOKING FOR

3–10 years of experience in ML research, applied ML, or ML systems engineering.
Deep familiarity with PyTorch, JAX, or TensorFlow, and hands-on experience with model architectures such as Transformers, Diffusion models, or RLHF.
Strong foundations in data processing, distributed training, and evaluation metrics.
Ability to translate research papers into working prototypes and production-ready code.
Curiosity about emerging ML paradigms — multimodality, self-learning, synthetic data, agentic systems, and more.
Comfort and enthusiasm for building from zero to one in a high-velocity, unstructured startup environment.