Staff Data Scientist (Top 1%)

A well-funded AI company ($37M Series C) with roots in Caltech research is looking for a Staff Data Scientist to join their platform team. This isn't your typical tech role - you'll be building AI systems that support national readiness and security operations.

The Role

You'll be extending their AI platform into agentic frameworks using state-of-the-art generative AI methods. This means working with real customer data to develop AI solutions from concept through production deployment, with a focus on maintaining operational readiness across critical infrastructure.

The company has introduced innovative approaches to analytics and works closely with government and defense clients. Your work will directly impact maintenance operations, materiel readiness, and cyber security insights.

What You'll Do

• Build and deploy agentic AI systems using modern GenAI frameworks
• Develop deep learning solutions for government/defense customers
• Own full solution lifecycle from ideation to production
• Work with big data frameworks (Databricks, etc.) for enterprise-scale solutions
• Travel to client sites and military installations as needed

Requirements:

• 3+ years production Python development
• 3+ years Python data stack (pandas, numpy, etc.)
• 2+ years deploying ML models in production
• 2+ years PyTorch/TensorFlow for deep learning
• 1+ year developing solutions with Large Language Models
• Experience with GenAI frameworks (LangChain, vector databases, etc.)

What's On Offer

• Flexible hours with 10am-4pm core time
• Clear career progression paths
• Full benefits including medical, dental, vision.
• Unlimited paid time off.
• Work on high-impact projects with measurable outcomes

Company Background

• 100-person team, founded 2016, built on decade of research partnership with NASA/JPL. They've developed proprietary AI platform technology and work with government agencies on mission-critical applications.

If you read the full job advert and are SUPER interested, connect with me and add a note that includes the answer to the following:

Imagine you’re building a model with dynamic computation graphs — which tool are you reaching for? (A) TensorFlow (B) NumPy (C) PyTorch

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