Responsibilities
- Team leadership & management: Build, mentor, and grow a team of research scientists and research engineers
- Technical Strategy & Execution: Oversee work across the full LLM post-training stack. Influence the technical roadmap and research direction. Translate ambiguous user and product needs into tractable research questions, technical plans, and measurable outcomes
- Cross-functional collaboration: Lead complex cross-functional projects end-to-end
- Hands-on technical contributions: Maintain technical credibility through hands-on contributions to critical projects. Review code, provide technical guidance, and unblock complex scaling or modeling challenges. Define and maintain clear research quality standards and engineering best practices and engineering standards for the team
Minimum Qualifications
- PhD degree in Computer Science, Machine Learning, or a related technical field
- 6+ years of industry experience in AI research, machine learning, or a closely related technical field
- 4+ years of experience managing teams of researchers or engineers, including experience managing other technical leaders or managers
- Experience working on frontier-quality/state-of-the-art Large Language Models. Deep, practical experience in LLM post-training
- Demonstrated ability to balance hands-on technical work with people management and strategic planning
- Experience communicating technical strategy and research direction to cross-functional stakeholders
- Publications at peer-reviewed venues (NeurIPS, ICML, ICLR, ACL, EMNLP, or similar) related to deep learning, language models, or data-centric AI
Preferred Qualifications
- Hands-on experience managing teams that build language model post-training pipelines (SFT/RLHF/RLVR), synthetic data generation, or high-quality evals infrastructure
- Experience in implementing or developing environments for agentive workflows (e.g., tool use, web browsing environments, coding sandboxes)
- Extensive experience working on long horizon agents, agent tool use, personalization, and/or search
- Experience building infrastructure for agentive workflows, tool-use data collection, or reinforcement learning environments
- Experience building and scaling large-scale distributed systems and high-throughput data processing pipelines
- Experience managing teams in fast-paced research or startup environments
$219,000/year to $301,000/year + bonus + equity + benefits
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