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micro1

Forward Deployed Engineer

$150K - $250K Remote Mid Posted June 10, 2026

Role Description

Core team $250K - $400K/yr compensation Required Skills -------------------

Python LLM Systems ML Infrastructure RAG/AI Automation

About micro1

micro1 is the leading AI data lab for training frontier models and evaluating AI agents. Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more. micro1 transforms that real-world expertise into high-quality training data, evaluations, and feedback loops that improve how AI systems learn, reason, and perform. Our platform identifies and vets top talent through an AI recruiter, enabling high-quality expert contributions at scale. We aim to enable 1 billion people to do meaningful work by applying their expertise to AI. As our global expert network grows, micro1 is building the human intelligence layer for frontier AI.

Job Description -------------------

Job Title: Forward Deployed Engineer

Job Type: Full-time

Location: Remote / Travel-required

The Role

We’re hiring a Member of Technical Staff, Forward Deployed to work directly with the world’s leading AI labs and enterprises as a technical research and implementation partner. This role sits at the intersection of applied AI, ML infrastructure, data intelligence, and partner-facing product development.

You’ll help strategic partners define research directions, structure and curate high-quality data, implement ML and evaluation pipelines, and build the agentic systems that extend multi-turn agents and workflows in production. You should be comfortable moving between ambiguous research questions, technical architecture, hands-on engineering, and partner-facing execution.

What You’ll Work On

  • 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, including discovery, architecture, implementation, deployment, reliability, iteration, and partner success.

What We're Looking For

  • 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.

Preferred Qualifications

  • 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.

Compensation & Benefits Notice

The national pay range for this full-time position is base salary of $150,000 –$250,000 USD. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.

micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to [email protected].

Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

Disclaimer

The information contained in this job posting, including but not limited to role responsibilities, qualifications, compensation, and benefits, is provided for informational purposes only and does not constitute a binding offer of employment. micro1 reserves the right to amend, modify, or withdraw any portion of this posting at its sole discretion and without prior notice. All employment decisions are made in accordance with applicable laws and regulations.

About Forward Deployed Engineering

Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.

Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.

Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.

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