Senior Machine Learning Engineer, Agentic Systems (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 Senior Machine Learning Engineer, Agentic Systems, you will be a core contributor to both customer-driven projects and the enduring products of the company. Working directly with customers, data scientists, software engineers, and DevOps engineers, you’ll develop machine learning solutions, orchestrate complex data and agentic workflows, and help shape the capabilities of Chariot, our proprietary AI operations platform. You’ll work across models, agents, tools, and workflows to build reliable and effective AI systems while analyzing and improving their performance and usability. Your work will inform platform improvements, expand our capabilities, and extend to the field through mission-critical deployments, direct customer contact, and insights that shape what we build next. 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 6+ years relevant experience - Demonstrated experience delivering data-centric systems (e. g. , data engineering, data cleaning, ETL pipelines, machine learning, and other production analytics) - Experience designing, building, evaluating, and optimizing LLM-powered agents and agentic workflows - Experience integrating AI systems with external tools, APIs, MCP servers, and enterprise data sources - Experience developing evaluation approaches and frameworks to measure agent performance, reliability, and safety - Familiarity with retrieval-augmented generation (RAG), tool use, planning, and multi-agent architectures - Proficiency in programming languages and libraries common to machine learning; excellence in Python is essential, as is knowledge of libraries like TensorFlow, PyTorch, and/or scikit-learn - Strong software engineering fundamentals—including algorithms, data structures, and design patterns—as well as proficiency in at least one systems programming language (e. g. , Go, Rust, C++, Java, Scala) - Proficiency with modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging, etc. ) - 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 (e. g. , asynchronous programming, REST, GraphQL, React, Vue, Angular) - Experience implementing and deploying software into containerized or cloud environments (e. g. , Docker, Kubernetes [K8s], infrastructure as code, major cloud architectures) - Experience with a variety of unstructured data types (e. g.