Founding Engineer - ML Research
Summary
Founding ML research engineer at a Series A AI data and evaluation company: day to day you design, train, and evaluate models (LLMs, diffusion), build scalable training/eval pipelines, and set research standards alongside the founding team. Core stack: PyTorch/JAX/TensorFlow, distributed training, foundation-model evaluation. On-site in Mountain View.
About the Role
This is a founding-level ML Research Engineer position at a Series A AI data and evaluation company serving top frontier AI labs and enterprises. You will work directly with the founding team to build and scale the research backbone from the ground up, sitting at the intersection of applied machine learning and systems engineering. The role carries real influence over technical culture and research direction at an early, high-growth stage.
What You'll Do
Design, train, and evaluate ML models including LLMs, diffusion models, and domain-specific architectures.
Build scalable experimentation pipelines covering 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.
Set foundational standards for research rigor, documentation, and reproducibility across the team.
What We're Looking For
3 to 10 years of hands-on experience in ML research, applied ML, or ML systems engineering, with substantive research depth (not primarily conventional applied engineering).
Research lab or leading AI/technology company experience, including model training, benchmark development, and foundation-model evaluation or red-teaming.
Experience writing or contributing to research papers is strongly valued.
Deep fluency with PyTorch, JAX, or TensorFlow, and strong knowledge of model architectures such as Transformers, Diffusion models, or RLHF.
Solid foundations in data processing, distributed training, and evaluation metrics.
Ability to move fluidly from research papers to working prototypes to production-ready code.
Curiosity for emerging paradigms such as multimodality, self-learning, synthetic data, and agentic systems.
Comfort building from zero to one in a fast-moving, unstructured startup environment.
Compensation & Benefits
Base salary: $220,000 to $300,000 USD annually, plus equity. Visa sponsorship is not available for this role.
Location
On-site in Mountain View, California, United States.
Skills
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