About the Role
This is a founding-team ML engineering role at an early-stage AI data and services company, building core machine learning systems from scratch for frontier AI labs and enterprises. You'll bridge research and production engineering, owning the full ML lifecycle while directly shaping technical culture and infrastructure.
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, cloud ML infrastructure (AWS, GCP, or Azure), and 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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Strong bias for action, ability to work autonomously, and genuine eagerness to build from the ground up.
Compensation & Benefits
Base salary range: $220,000 – $300,000 USD annually. Visa sponsorship is not available for this role.
Location
On-site in Mountain View, California, United States.
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