Forward Deployed Engineer | $300K - $650K/yr | Remote
Listing checked July 7, 2026 · pay as published by Micro1
Overview
This role sits at the intersection of applied AI, ML infrastructure, and partner-facing product development. You'll work directly with leading AI labs and enterprises to transform ambiguous research questions into production-grade systems. As a Member of Technical Staff, you'll own everything from data curation and LLM agent workflows to deployment and partner success — all while operating in a fast-moving, remote-first environment.
What You'll Do8
- 1Partner with top-tier AI labs and enterprise clients to define research goals, technical requirements, and project roadmaps.
- 2Build and scale data intelligence systems that collect, organize, evaluate, and continuously improve training and evaluation data.
- 3Implement ML pipelines for data curation, model training, experimentation, and iterative improvement.
- 4Design data taxonomies, labeling frameworks, and quality standards that enhance dataset structure and model performance.
- 5Develop LLM-powered applications including multi-agent systems, tool-using agents, RAG workflows, and human-in-the-loop evaluation harnesses.
- 6Translate vague AI problems into scoped technical projects and production systems alongside research and engineering teams.
- 7Build inference and experimentation infrastructure to support frontier AI model deployment and evaluation.
- 8Own systems across the full lifecycle — from discovery and architecture to deployment, reliability, and ongoing partner success.
Requirements6
- 1Strong Python engineering skills with a proven ability to build and ship production systems end to end.
- 2Hands-on experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation.
- 3Ability to operate independently in ambiguous, partner-facing settings with strong technical and product ownership.
- 4Experience building or maintaining data pipelines, ML infrastructure, evaluation systems, or research workflows.
- 5Solid understanding of data quality, taxonomy design, labeling workflows, and dataset curation for AI systems.
- 6Comfortable working directly with technical partners, researchers, founders, and enterprise stakeholders.
Who Should Apply
This role is for a builder who thrives at the intersection of research and engineering — someone who can navigate ambiguity, own complex systems end to end, and collaborate directly with world-class AI labs. You're equally comfortable architecting ML infrastructure and writing production Python code, and you bring a track record of delivering LLM-based solutions (like multi-agent systems or RAG pipelines) into real-world use. If you enjoy being the bridge between cutting-edge research and scalable production, this is your opportunity.
Salary Insight
The base salary range for this full-time position is $180,000–$250,000 USD, plus equity compensation and performance-based bonuses. micro1 also offers up to 100% health-insurance premium reimbursement, paid time off, and a 401(k) with company match.
Pay and demand for AI Training roles
AggregatedTypical pay
$74/hour
This role
$350K–650K ≈ $175–$325/hr
Most AI Training roles pay $42–$98 per hour. This role's pay sits above that range.
Based on 1,104 similar roles that publish pay · 302 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 1,283
- Listed in last 30 days
- 502
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- 97%
Hiring most right now: micro1 (374) · SME Careers (254) · Mercor (161)
Most requested skills · share of roles
- ai training13%
- llm evaluation11%
- ai evaluation11%
- data annotation9%
Figures from AI Training roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.
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Compensation
$350K–650K
Required Skills
Application Tip
In your application, include a brief case study of a production LLM system you built — highlight how you handled ambiguous requirements, designed the data pipeline, and measured performance with evaluation harnesses.
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