Senior Machine Learning Engineer–Field Engagement (AI)

Location: Hybrid/on-site near customer sites in Washington, DC; Seattle, WA; or Raleigh-Durham, NC. Travel up to 25% of the time. US citizenship and eligibility and willingness to obtain and maintain a Secret or above US security clearance are required.

Base pay: $160,000–$200,000/year, plus equity grants and cash bonuses.

Company

hirify.global develops Chariot, an AI product suite that monitors model performance, manages drift, and supports production AI systems in real-world and government use cases.

What you will do

• Develop deep hands-on expertise across Chariot Core, Forge, and vertical solutions, and run customer trials end to end.
• Lead customer onboarding, demos, workshops, and use-case implementation by configuring models, datasets, agents, and workflows.
• Co-build production AI systems and agents with customer engineering and machine learning teams from prototype through rollout.
• Develop ETL pipelines, scripts, and custom integrations to ingest customer data and connect existing processes to inference endpoints.
• Own the technical customer relationship during evaluations and early deployments, troubleshooting issues and resolving blockers.
• Partner with Product and Engineering on platform improvements, feature launches, documentation, example code, guides, and proof-of-concept templates.

Requirements

• Degree in computer science, engineering, or a similar technical field.
• 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 across software engineering, data science, ML engineering, and infrastructure teams.
• Strong software engineering skills, including writing, reading, and debugging production-quality Python and building pragmatic integrations, prototypes, and tooling.
• Ability to independently understand ambiguous problems, develop a plan, and execute without detailed step-by-step direction.
• Experience deploying AI models and agents at scale, including 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.

Nice to have

• Experience 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.

Culture & Benefits

• Trust, respect, candid and constructive communication, and personal ownership of collective results.
• Medical, dental, and vision insurance.
• Voluntary life, long-term disability, accident, and hospital indemnity insurance.
• HSA and FSA plans, including dependent care FSA, plus a 401(k) plan.
• Unlimited PTO and paid parental leave.