About the Role
This is a founding-level ML engineering role at an early-stage AI data and services company, building core machine learning systems from the ground up alongside a small, high-ownership team. You'll bridge research and engineering to design, train, and ship production-grade models that directly serve frontier AI labs — and you'll help shape the technical culture and infrastructure from day one.
What You'll Do
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Build and optimize end-to-end ML pipelines, from data ingestion through 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 experience as an ML Engineer, Applied Scientist, or Research Engineer.
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Proficiency in Python and at least one major ML framework: PyTorch, TensorFlow, or JAX.
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Strong grasp of ML fundamentals — data preprocessing, feature engineering, model training, and optimization.
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Hands-on experience with distributed systems and cloud ML infrastructure (AWS, GCP, or Azure).
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Familiarity with MLOps tooling such as Weights & Biases or MLflow.
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Comfort working with large datasets and high-throughput systems.
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A bias for action, ability to work autonomously, and genuine enthusiasm for building from scratch.
Compensation & Benefits
Base salary of $220,000 – $300,000 USD annually. Visa sponsorship is not available for this role.
Location
On-site in Mountain View, California, United States.