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
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Design, train, and evaluate ML models — including LLMs, diffusion models, and domain-specific architectures.
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Develop scalable experimentation pipelines spanning data ingestion, model training, and evaluation workflows.
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Collaborate with data and infrastructure teams to optimize training throughput and model quality.
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Contribute to open research, internal benchmarks, and new techniques in multimodal and generative AI.
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Rapidly prototype research ideas and productionize them into usable models and tools.
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Define and promote standards for research rigor, documentation, and reproducibility across the engineering org.
What We're Looking For
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3–10 years of experience in ML research, applied ML, or ML systems engineering.
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Deep familiarity with PyTorch, JAX, or TensorFlow and hands-on experience with architectures such as Transformers, Diffusion models, or RLHF.
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Strong foundations in data processing, distributed training, and evaluation metrics.
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Demonstrated ability to move from research papers to working prototypes to production-ready code.
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Curiosity about emerging ML paradigms — multimodality, self-learning, synthetic data, and agentic systems.
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Passion for building from zero to one in a high-velocity startup environment.
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Nice to have: Open research contributions, published benchmarks, or peer-reviewed publications in relevant ML research areas.
Compensation & Benefits
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Salary: $220,000 – $300,000 USD annually
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Equity participation as a founding team member
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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.
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