Design, train, and evaluate ML models — including LLMs, diffusion models, and domain-specific architectures.
Develop scalable experimentation pipelines spanning data ingestion, model training, and evaluation workflows.
Collaborate with data and infrastructure teams to optimize training throughput and model quality.
Contribute to open research, internal benchmarks, and new techniques in multimodal and generative AI.
Rapidly prototype research ideas and productionize them into usable models and tools.
Define and promote standards for research rigor, documentation, and reproducibility across the engineering org.

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 architectures such as Transformers, Diffusion models, or RLHF.
Strong foundations in data processing, distributed training, and evaluation metrics.
Demonstrated ability to move from research papers to working prototypes to production-ready code.
Curiosity about emerging ML paradigms — multimodality, self-learning, synthetic data, and agentic systems.
Passion for building from zero to one in a high-velocity startup environment.
Nice to have: Open research contributions, published benchmarks, or peer-reviewed publications in relevant ML research areas.

Equity participation as a founding team member
Visa sponsorship: Not available — candidates must be authorized to work in the US

Founding Engineer – ML Research

Key skills