Machine Learning Engineer (Active TS/SCI Clearance)

“In 36 months, agentic AI systems will be an operating reality across major institutions. We intend to be central to it.” — Dr. Jim Rebesco, Cofounder and CEO, Striveworks

The government’s demand for AI is growing far faster than the systems required to support it. Fewer than 15% of federal AI programs have reached sustained production, despite billions of dollars invested. The models perform in testing, but they degrade in the real world. And when performance drops, trust goes with it.

Striveworks was built to solve that problem.

What you’ll build

Since 2018, we have delivered the most trusted AI systems operating in real-world use cases—providing a layer of assurance underneath hundreds of deployed models that monitors performance, manages drift, and sustains systems long after they leave the lab.

As a Machine Learning Engineer, you will be a core contributor to both customer-driven projects and the enduring products of the company. Working alongside data scientists, software engineers, and DevOps engineers, you’ll develop machine learning models, custom analytics, and mission-critical solutions applied to image, video, text, geospatial, time series, structured, and other data. You will orchestrate complex data engineering pipelines in agentic and traditional architectures. Your work will inform the future of Chariot, our proprietary AI operations platform, and your impact will be felt on mission-critical deployments and through direct customer contact.

What it’s like here

We lead with trust, treat each other with respect, and use candor consistently, kindly, and constructively. We care deeply about our work, and we find genuine satisfaction in doing it well. Above all, we take ownership—because we feel the weight of collective results personally. We are looking for people who share these values and are eager to put them into practice.

What we’re looking for

• A BS degree in computer science, machine learning, or a related discipline and 2+ years of relevant experience

• Proficiency in Python and libraries like PyTorch, TensorFlow, and/or scikit-learn

• Proficiency in software engineering fundamentals and at least one systems programming language (e.g., Go, Rust, C++, Java, Scala, etc.)

• Experience contributing to data-centric systems (e.g., data engineering, data cleaning, ETL pipelines, machine learning, and other production analytics)

• Exposure to modern software engineering tools and processes

• Active TS/SCI security clearance and US citizenship

The following isn’t required, but we’d love to see it:

• An advanced degree in data science, machine learning, computer science, or a related discipline