Manager, Forward Deployed Engineering

Main Responsibilities

• Build and lead the Forward Deployed Engineering function, defining the team’s operating model, technical roadmap, and standards for execution
• Hire, mentor, and develop a high-performing team, fostering strong ownership, velocity, and engineering excellence
• Own the design and evolution of end-to-end AI data pipelines, including dataset generation, evaluation systems, and quality frameworks
• Drive development of ML-assisted and HITL workflows that improve the speed, scalability, and reliability of data production
• Establish and standardize measurement, benchmarking, and validation systems to ensure consistent, high-quality dataset delivery
• Develop scalable internal tooling and workflows for orchestration, monitoring, and delivery, reducing bottlenecks and improving throughput
• Partner cross-functionally with Delivery, Engineering, and Research to productionize new approaches and continuously improve DaaS capabilities

What We’re Looking For

• 6+ years of experience in AI/ML/GenAI engineering, forward deployed engineering, solution engineering or applied AI roles, with strong hands-on technical depth
• 2+ years of experience leading or managing engineers in a hands-on, player-coach capacity
• Strong experience building and operating data pipelines and ML/LLM-based workflows in production environments
• Proficiency in Python, SQL, and modern data tooling; experience integrating systems via APIs. Has written code in the last 2 years
• Experience designing evaluation frameworks, quality measurement systems, or benchmarking approaches for ML systems
• Strong systems thinking — ability to design scalable, reusable solutions rather than one-off implementations
• Proven ability to operate in ambiguous, fast-paced environments and drive execution across cross-functional teams. Start-up or growth-stage experience is strongly preferred.
• Customer-facing, consulting, or services experience is strongly preferred

Nice to have

• Experience with human-in-the-loop (HITL) systems, data annotation workflows, or data-centric AI approaches.
• Familiarity with synthetic data generation or model-assisted labeling techniques.
• Experience building internal platforms or tooling for delivery or operations teams