Founding Engineer – ML Research

About the Role A well-funded Series A AI/ML platform company is looking for a Founding Engineer — ML Research to build and scale its research backbone. You'll bridge applied machine learning and systems engineering, turning cutting-edge ideas into reliable, production-ready models. As an early team member, you'll have direct impact on model performance, data quality, and the company's core technical foundations in generative and multimodal AI. What You'll Do 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. 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 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. Compensation & Benefits Salary: $220,000 – $300,000 USD annually Equity participation as a founding team member Visa sponsorship: Not available — candidates must be authorized to work in the US Location This is an on-site role based in the San Francisco Bay Area / Mountain View, California . Remote arrangements are not available for this position. Originally posted on Himalayas