Forward Deployed Engineering Manager

About the role

You will lead the end-to-end ownership of the Forward Deployed Engineering team, defining hiring strategy, coaching technical craft, and managing performance across the FDE track and into FDR or General Management paths. You will own the supply side of FDE staffing, committing talent to the staffing cadence and matching skills to projects while balancing vertical preferences with project phase needs. You will pressure-test project scope and instructions to ensure data programs genuinely move customer models and define clear quality measurement early rather than late. You will coach FDEs on scoping tasks, pipeline design, and annotation guidelines to uphold technical excellence and consistency across customer deliverables. You will keep FDEs at the appropriate altitude by shielding them from day-to-day operations and actively delegating project execution to SPLs and Pod Leads. You will partner closely with the Services lead, SPL Manager, Deployment Leads, and General Managers to align on priorities and resource allocation. You will steward the FDE career path, owning onboarding and development from FDE through FDE 2 to FDE Manager and supporting growth toward specialized and leadership tracks. You will maintain hands-on technical depth to set and defend craft standards, staying close to the work enough to coach on pipeline design and instruction quality. You will drive a culture of high agency, rapid execution, and continuous learning where impact is tied directly to contribution and clear ownership.

Key facts

What you'll do

Lead the FDE team end-to-end including hiring, coaching, performance management, and career development across the FDE track and toward FDR or General Management.

Own the supply side of FDE staffing by committing FDEs to the staffing cadence and matching them to projects based on skill and development needs while balancing vertical preferences with project phase.

Pressure-test project scope and instructions to ensure customer data programs genuinely move models and define quality measurement early in the lifecycle.

Coach FDEs on scoping tasks, designing pipelines, and writing instructions that guide Alignerrs to produce high-quality training signals.

Keep the FDE team at the right altitude by shielding them from operational noise and actively delegating day-to-day execution to SPLs and Pod Leads.

Partner with the Services lead, SPL Manager, Deployment Leads, and General Managers to align on staffing, priorities, and cross-functional execution.

Steward the FDE career path and own the onboarding experience, clarifying roles from FDE to FDE 2 to FDE Manager and branching toward FDR or General Management.

Maintain technical depth in AI data programs to set craft standards, review pipeline design, and pressure-test instructions for clarity and measurability.

Drive a culture of ownership, rapid execution, and continuous learning where impact is directly tied to contribution and measurable results.

Ensure FDEs focus on high-level technical and customer-facing work while operations are handled by downstream pod leadership.

Define and enforce quality bar early in projects, emphasizing measurement and validation before scaling annotation efforts.

Balance team capacity, skill development, and project demands to optimize utilization and long-term retention of technical talent.

Champion data-centric thinking across the organization by translating customer goals into precise training signals and evaluation metrics.

Support the growth of the Expert Marketplace and Frontier Data services by aligning annotation strategy with domain expertise and client needs.

Act as a player-coach, contributing directly to complex scoping and pipeline design while leading people management and strategy.

Requirements

8+ years of experience in software engineering, data programs, or technical leadership roles relevant to AI or ML workflows.

Demonstrated ability to lead and develop technical teams, with experience managing engineers through performance coaching and career progression.

Strong understanding of AI data pipelines, annotation methodologies, and the challenges of producing high-quality training data at scale.

Exceptional written and verbal communication skills to translate complex technical concepts for both technical and non-technical stakeholders.

Proven track record of owning end-to-end delivery on complex projects, balancing scope, quality, and timeline constraints.

Experience working with frontier AI models and data-centric approaches that shape model behavior and training signals.

Comfort operating in a fast-paced, high-growth environment where processes are built as you scale and ambiguity is common.

Commitment to continuous learning and deep ownership, with the ability to raise the craft bar for data quality and measurement.

Practical notes

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