Senior Machine Learning Engineer–Field Engagement

“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–Field Engagement at Striveworks, you’ll work directly with our customers during trials and early deployments of Chariot, our AI product suite spanning Chariot Core (our AIOps platform), Forge (our agentic harness), and vertical solutions. You’ll own the technical journey from first log in to real value—building the integrations, pipelines, and configurations that help customers solve meaningful problems with Chariot.

You’ll carry that technical context from pilot through post-sale, helping ensure that successful evaluations turn into lasting adoption.

Between customer engagements, you’ll have the freedom to invest in high-value work shaped by what you’ve learned in the field: polishing rough edges in the product, contributing to platform improvements, building better onboarding and technical resources, and writing technical blogs and papers about new and exciting applications of our products or research with them.

Your day-to-day will include:

Becoming an expert in the Chariot product suite:

Develop deep, hands-on fluency across Chariot Core, Forge, and our vertical solutions so that you can speak credibly to any part of the product and run a trial end to end.

Getting customers to value quickly:

Run hands-on onboarding, demos, and workshops in the context of each customer’s actual use case. Identify the highest-value problem to solve and then proactively drive it to working status, configuring the relevant models, datasets, agents, and workflows so customers start with a working environment rather than an empty platform.

Co-building with customer engineering teams:

Work alongside customer engineers and machine learning (ML) teams to design and build production AI systems and agents on Chariot, from initial prototype through rollout.

Building data pipelines and integrations:

Develop ETL pipelines, scripts, and custom integrations to ingest customer data and connect their existing processes to our inference endpoints.

Owning the technical relationship:

Serve as the customer’s primary technical point of contact throughout the evaluation and early deployment. Troubleshoot issues directly, remove technical blockers, and own problems through resolution.

Advising customers beyond the initial deployment:

Help customers make sound architecture and implementation decisions, establish best practices, and understand relevant product capabilities and roadmap directions. Surface opportunities for deeper adoption and expansion for the Sales and BD teams to pursue.

Partnering with Product and Engineering:

Build fast-turnaround, high-value platform improvements alongside engineering when a trial needs something the product doesn’t yet do, and support the launch of new features with the field perspective.

Scaling what you learn:

Surface field feedback to the Product team, and build reusable assets such as example code, guides, documentation, and proof-of-concept templates.

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 degree in a technical field (computer science, engineering, or similar)
• 6+ years of experience as an ML engineer, data scientist, forward-deployed engineer, or in a similar hands-on technical role, ideally with direct customer exposure
• Strong technical fluency in AI and ML systems, with the ability to work effectively across software engineering, data science, ML engineering, and infrastructure teams
• Strong software engineering skills, including the ability to write, read, and debug production-quality Python and build pragmatic integrations, prototypes, and tooling
• Comfort with ambiguity: the ability to independently understand a problem, develop a plan, and execute without detailed step-by-step direction
• Experience deploying AI models and agents at scale
• Experience with AI model and agent evaluation, observability, or guardrails
• Strong customer communication skills and the ability to build trust with technical stakeholders
• US citizenship and eligibility and willingness to obtain and maintain a Secret (or above) US security clearance
• The following isn’t required, but we’d love to see it: Experience with deploying large language models using vLLM
• Familiarity with cloud infrastructure, Kubernetes, APIs, distributed systems, or modern data and ML platforms
• Experience leading, managing, or mentoring small, cross-functional engineering teams
• Active Secret (or above) US security clearance

This position offers a hybrid/on-site work environment near one of our customer sites in the Washington, DC, Seattle, WA, or Raleigh-Durham, NC, area. You will be expected to travel up to 25% of the time.