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 i...

Striveworks was built to solve that problem.

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.

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.
Striveworks is an Equal Opportunity Employer and does not discriminate in employment on the basis of race, color, religion, belief, sex (including pregnancy and gender identity or expression), national origin, social or ethnic origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors. Striveworks will not tolerate discrimination or harassment of any kind.
If you require assistance or a reasonable accommodation in the application process, please contact People Operations at hr@striveworks.us.
In compliance with federal law, all persons hired will be required to verify their identity and eligibility to work in the United States and to complete an employment eligibility verification form upon hire.
Striveworks is a participating employer in the E-Verify program.

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.