Forward Deployed Engineer

Stack

Responsibilities

• ▹Work directly with leading AI labs and enterprise partners to define research goals, technical requirements and project direction
• ▹Build large-scale data intelligence systems for collecting, organizing, evaluating and improving training and evaluation data
• ▹Implement ML pipelines for data curation, model training, evaluation, experimentation and continuous improvement
• ▹Design data taxonomies, labeling systems and quality frameworks that improve dataset structure, model performance and research outcomes
• ▹Develop LLM applications, including multi-agent systems, tool-using agents, RAG workflows, evaluation harnesses and human-in-the-loop systems
• ▹Partner with research and engineering teams to translate ambiguous AI problems into scoped technical projects and production systems
• ▹Develop infrastructure for model inference, experimentation, evaluation and deployment across frontier AI platforms
• ▹Build systems that help partners move from one-off AI experiments to reliable, repeatable multi-turn agent workflows
• ▹Own systems across the full lifecycle: discovery, architecture, implementation, deployment, reliability, iteration and partner success

Requirements

• ▹Able to operate independently in ambiguous, partner-facing settings with strong technical and product ownership
• ▹Strong Python engineer with experience building and shipping production systems end to end
• ▹Experience working with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation
• ▹Built or maintained data pipelines, ML infrastructure, evaluation systems, or research workflows
• ▹Strong understanding of data quality, taxonomy design, labeling workflows and dataset curation for AI systems
• ▹Comfortable working directly with technical partners, researchers, founders and enterprise stakeholders

Nice to have

• ▹Background at a startup, AI infrastructure company, applied AI company or research-focused engineering team
• ▹Experience building systems for multi-turn agents, agent evaluation, workflow automation or human-in-the-loop AI
• ▹Experience designing data taxonomies, annotation systems, evaluation rubrics or dataset quality pipelines
• ▹Experience acting as a technical partner to external customers, research teams or strategic enterprise accounts
• ▹Familiarity with modern LLM tooling, agent frameworks, model evaluation stacks and ML experimentation platforms

Soft skills

What we offer

• ▹Base salary of $180,000–$250,000 USD (national range)
• ▹Equity compensation for all employees
• ▹Performance-based bonuses possible
• ▹Up to 100% reimbursement of health-insurance premiums
• ▹Paid time off
• ▹401(k) plan with company match
• ▹Remote-first working environment

About the company

A rapidly growing, venture-backed AI company (more than $40 million in funding) that uses a global network of experts to help leading AI organizations build, evaluate and improve models.