Perplexity

Member of Technical Staff (AI Research Lead)

🇺🇸 San Francisco, Stany Zjednoczone Na miejscu Opublikowano Kwi 13, 2026
Lokalizacja San Francisco, Stany Zjednoczone
Tryb pracy Na miejscu
Język English
Opublikowano 13 kwietnia 2026
Ostatnio sprawdzono 28 maja 2026
Kontekst JobGrid

Podsumowanie roli od JobGrid

Member of Technical Staff (AI Research Lead) at Perplexity: San Francisco, Stany Zjednoczone; Na miejscu. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.

  • Location and workplace: San Francisco, Stany Zjednoczone, Na miejscu
  • Source freshness: checked by JobGrid on 2026-05-28.
  • Application path: candidates continue to the employer application page with non-personal referral tags.

Perplexity is seeking an exceptional AI Research Tech Lead to drive our research strategy and lead the development of our in-house Online LLMs, the Sonar models. In this leadership role, you will set the macro research direction across different modalities, mentor a team of researchers, and take advantage of our rich query/answer dataset to continue scaling our Sonar model performance and deliver the SOTA Online LLM experience to our users.

Responsibilities

Research Leadership & Strategy

  • Define and execute the macro research direction across multiple modalities, including post-training LLMs for agent trajectories and future mid-training initiatives

  • Lead strategic research planning and roadmap development to advance Sonar model capabilities

  • Drive innovation in supervised and reinforcement learning techniques for query answering

  • Collaborate with leadership to align research priorities with product and business objectives

Team Development & Mentorship

  • Coach and mentor a team of AI research scientists and engineers, fostering their technical and professional growth

  • Establish the long-term macro research direction across the team, including our direction across different modalities

  • Lead hiring and onboarding of new research talent

  • Create a collaborative environment that encourages knowledge sharing and innovation

Technical Excellence

  • Post-train SOTA LLMs on query answering using cutting-edge supervised and reinforcement learning techniques

  • Own and optimize the full stack data, training, and evaluation pipelines required for LLM post-training

  • Deliver Sonar models that provide SOTA query answering performance

  • Drive research into agent trajectories and multi-modal capabilities

  • Lead the technical roadmap for eventual mid-training investments

Cross-Functional Collaboration

  • Work closely with engineering teams to integrate Sonar models into our product

  • Partner with product teams to understand user needs and translate them into research priorities

  • Collaborate with data teams to leverage our unique query/answer dataset effectively

  • Communicate research progress and findings to stakeholders across the organization

Qualifications

Required

  • Minimum of 5 years of experience working on relevant AI/ML projects with 3**+ years in a technical leadership role**

  • Proven track record of leading and mentoring technical and research teams

  • A Computer Science graduate degree at a premier academic intitution

  • Deep expertise with large-scale LLMs and Deep Learning systems

  • Strong programming skills with versatility across multiple languages and frameworks

  • Demonstrated ability to set technical vision and drive execution

  • Experience with pre-training and post-training techniques (self-supervised learning along with SFT/DPO/GRPO/PPO)

  • Self-starter with exceptional ownership mentality and ability to work in ambiguous environments

  • Passion for solving challenging problems and pushing the boundaries of AI research

Nice-to-have

  • PhD in Machine Learning, Computer Science, or related areas

  • Experience with agent-based AI systems and multi-modal model development

  • Background in mid-training or pre-training of large language models

  • Publications in top-tier AI/ML conferences

  • Experience in fast-paced startup environments

  • Track record of translating research into production systems