Head of AI Training Research

Leadership & Commercial Accountability

• Lead and align a cross-functional pod of applied researchers, FDEs, and project leads around shared client outcome goals
• Own the research practice's contribution to revenue — including supporting pre-sales, scoping engagements, and ensuring delivery quality that drives retention and expansion
• Act as an executive sponsor on strategic accounts, providing research credibility and depth to client relationships
• Build a team culture that is rigorous, fast-moving, and relentlessly focused on client impact

Client Delivery & Applied Research

• Define the applied research frameworks, workflows, and quality standards used across all client engagements
• Ensure applied researchers are translating benchmarking, data, and RL environment work directly into client-specific training solutions
• Partner with project leads to scope engagements accurately, manage research risk, and hit delivery milestones
• Work with FDEs to ensure research outputs are deployable, integrated, and producing measurable model improvements in client environments

Benchmarking

• Oversee the development of benchmarking capabilities used to demonstrate value to clients — pre- and post-engagement performance comparisons, capability gap analyses, and regression tracking
• Ensure benchmark design is tied to client-defined success metrics, not just internal research goals
• Use benchmark outputs as a feedback loop to continuously improve delivery quality across engagements

OTS Data & Data Strategy

• Lead strategy around sourcing, filtering, and deploying off-the-shelf datasets in support of client training objectives
• Build repeatable frameworks for data quality assessment that can be applied efficiently across diverse client use cases
• Identify reusable data assets and pipelines across engagements to improve margins and delivery speed

RL Environment Building

• Oversee the development of RL environments that are purpose-built or adapted for client-specific task performance
• Ensure environments are reproducible and portable across client deployments
• Partner with FDEs and applied researchers to close the loop between environment design and real-world client outcomes

Requirements

• 8+ years in applied ML or AI research, with at least 3 years leading research or technical delivery teams in a client-facing or revenue-generating context
• Proven track record of translating research capabilities — benchmarking, data curation, or RL — into delivered client value
• Experience working across applied researcher, engineering, and project management functions; comfortable orchestrating cross-functional pods toward a shared outcome
• Strong commercial instincts — able to scope, price, and communicate research work in terms of client ROI
• Deep familiarity with LLM training pipelines, including fine-tuning, RLHF/RLAIF, and evaluation methodology
• Excellent executive communication skills; confident representing the research practice in client conversations and during pre-sales

Nice to Have

• Prior experience at an AI services firm, applied research consultancy, or enterprise AI product company
• Familiarity with data licensing, synthetic data generation, or contamination detection in the context of client data
• Experience building scalable delivery infrastructure (templates, tooling, benchmarks) that improves team output across engagements