A Series A AI/ML startup focused on model training and evaluation is looking for a Founding ML Engineer to build and scale core machine learning systems from the ground up. This is a hands-on, high-impact role for someone who thrives at the intersection of research intuition and engineering execution. You will work closely with the founding team to design, train, and ship production-grade ML models while helping set the foundation for technical culture, infrastructure, and research practices.
What You’ll Do
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Build and optimize end-to-end ML pipelines, from data ingestion to deployment.
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Implement and fine-tune LLMs, embeddings, and generative models for real-world applications.
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Develop efficient training and inference systems leveraging distributed compute.
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Partner with data and product teams to translate ideas into measurable ML impact.
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Contribute to model monitoring, evaluation, and continual learning frameworks.
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Establish best practices in model versioning, reproducibility, and scalability.
What We’re Looking For
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3–10 years of hands-on experience as an ML Engineer, Applied Scientist, or Research Engineer.
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Proficiency in Python and at least one of PyTorch, TensorFlow, or JAX.
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Experience building production-grade end-to-end ML pipelines (data ingestion, training, deployment).
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Experience implementing and fine-tuning LLMs, embeddings, and generative models for real-world applications.
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Hands-on experience with distributed training/inference and scalable ML systems.
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Experience with cloud platforms (AWS, GCP, or Azure) and ML tooling (MLflow, Weights & Biases) for experimentation and model tracking.
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Strong collaboration skills; ability to work cross-functionally with data and product teams.
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Bias for action, autonomy, and eagerness to shape something from scratch.
Location
This role is on-site in Mountain View, California. Authorization to work in the US without visa sponsorship is required.
Compensation
$220,000 – $300,000 USD annually, commensurate with experience.