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Forward Deployed Engineer

$180,000–$250,000 + equityRemote, hiring in OregonFull-time

The Forward Deployed Engineer partners directly with leading AI labs and enterprises to turn ambiguous research goals into production-grade AI systems.

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At a glance

Posted base pay

$180K–$250KAbove the median

Midpoint $215K · median $190K

The work

Builds what the customer runs

Balance Mostly building

Project stages

  1. Pre-sales (not covered)
  2. Proof of concept (not covered)
  3. Implementation
  4. Operations

Travel Required, amount not stated

Coding
Writes production code
Builds for
External customers
Level
Senior
Role
Individual contributor

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Conditions

Location
Remote, OR
Work arrangement
Remote
Remote work restrictions
United States
Employment type
Full-time
Travel
Travel required, amount not stated
Pay
Pay details

The national pay range for this full-time position is base salary of $180,000 –$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.

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What you’ll do

  • Define research goals, technical requirements, and project direction with AI labs and enterprise partners
  • Build data intelligence systems, ML pipelines, taxonomies, labeling systems, and quality frameworks
  • Develop LLM applications including multi-agent systems, tool-using agents, RAG workflows, and evaluation harnesses
  • Translate ambiguous AI problems into scoped technical projects and production systems

What you’ll bring

  • Strong Python engineering experience building and shipping production systems end to end
  • Experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation
  • Experience building or maintaining data pipelines, ML infrastructure, evaluation systems, or research workflows
  • Strong understanding of data quality, taxonomy design, labeling workflows, and dataset curation for AI systems

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