Machine Learning Engineer (Active Secret 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 informs the future of Chariot, our proprietary AIOps platform, and your impact will be f [... source excerpt omitted ...] nguage (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 Secret (or above) US 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 Knowledge of relevant architectures and design patterns for client-server systems Experience implementing and deploying software into containerized or cloud environments via Docker, K8s, or similar Experi