Machine Learning Engineer

“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 Staff Machine Learning Engineer, you will be both a core contributor and a customer-facing technical leader. You’ll initiate the design, build, and delivery of systems—from data shaping and analytics to end-user delivery. Customers and engineers will bring you the hard problems. You’ll turn them into working systems and see them through to execution. The work will take you into the field, where direct customer input will 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

• An advanced degree in data science, machine learning, computer science, or a related discipline and 10+ years of relevant experience
• Proven ability to lead, manage, and mentor cross-functional engineering teams across office, remote, and customer environments
• Demonstrated experience defining, scoping, planning, and driving execution plans for complex, production-level technical solutions
• Strong communication skills and a track record of translating complex technical topics with clarity and professionalism to both internal engineering teams and external stakeholders 
• Broad proficiency in programming languages common to machine learning, with excellence in Python and at least one systems programming language
• Proficiency in the design and delivery of algorithms, data structures, and production analytics and in the use of design patterns in cloud environments