Research Lead (Coding QA)

Position Overview:

As a Research Lead you will lead the end-to-end execution of LLM training projects involving Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Reinforcement Learning from Execution Feedback (RLEF). You will lead a cross-functional team of AI Trainers, Leads, and Engineering Managers to ensure the delivery of high-quality data and model improvements.

You will serve as the senior-most researcher accountable for strategic client alignment, throughput, quality, and operational efficiency across multiple programming languages (Python, JavaScript, Java, etc.). A key aspect of this role includes leading our most important code workstream, while also providing support, and calibration guidance to other code teams.

Ideal Candidate Profile:

• US-based (West Coast preferred) 
• An exceptional communicator with experience working in large technical organizations. 
• Brings a deep understanding of how LLM researchers think and operate—enabling you to convert their often ambiguous instructions into structured, actionable workflows that drive high-quality data delivery.
• Brings technical fluency and operational leadership, enabling you to lead delivery in AI/LLM training projects without needing to code.
• Take initiative to unblock teams, address quality issues, and streamline workflows.
• Client-obsessed, with strong stakeholder instincts, the ability to proactively manage expectations, risks, and feedback loops.
• Have a continuous improvement mindset, always looking for ways to evolve team structure, workflows, tooling, and delivery KPIs.

Key Responsibilities:

1. Client Engagement & Operational Oversight

• Act as the strategic point of contact for clients - gathering requirements, aligning on quality expectations, and managing quality feedback loops.
• Proactively flag risks and drive resolution to ensure uninterrupted, high-quality delivery.
• Work closely with LLM researchers to deeply understand their thinking styles and objectives.
• Translate ambiguous or loosely defined research goals into clear, executable tasks for technical teams.
• Proactively flag risks and drive resolution to ensure uninterrupted, high-quality delivery.

2. Quality Governance & Continuous Improvement

• Ensure clarity, accuracy, and completeness in outputs: code, responses, explanations, and evaluations.
• Work closely with team leads and Client Researchers to implement quality review loops and resolve systemic quality gaps.
• Identify inefficiencies and continuously optimize workflows and operational structure.
• Ensure all output meets the highest standards expected by AI researchers and clients.
• Ensure technical soundness, review compliance, and strategic alignment across delivery streams.

Required Qualifications:

• 4+ years of experience as a Researcher or similar role within a technical or data-driven environment at a large company
• PhD or equivalent experience in Computer Science, Machine Learning, Artificial Intelligence, or a related field​
• Experience working closely with LLM researchers and familiarity with how they think, communicate, structure projects, and measure success
• Demonstrated success in translating ambiguous or high-level research directions into actionable plans for engineering and delivery teams.
• Proven ability to translate research ambiguity into clear tactical execution plans
• Strong background leading technical delivery efforts and code-heavy teams
• Effective communicator, both in async and live settings, across technical and non-technical audiences.
• Working knowledge of Python and/or JavaScript to effectively engage with Engineering Managers and assess code-level output quality.
• Exceptional communication and documentation skills - comfortable leading async and live updates across technical and non-technical audiences.
• Strong decision-making, prioritization, and conflict-resolution abilities in dynamic, high-stakes environments.

Preferred/Bonus Qualifications:

• Background in Machine Learning or Data Science is a plus.
• Familiarity with LLM concepts, training cycles, or evaluation methods (e.g., RLHF, SFT, RAG).
• Hands-on experience with LLM APIs (GPT, Gemini, Claude, etc.) and RAG workflows.