Research Engineering Manager, Perplexity AI

• Proven experience with large-scale LLMs and Deep Learning systems
• Strong Python and PyTorch skills; versatility across languages and frameworks is a plus
• Experience leading or managing research or engineering teams working on large-scale AI model development, including driving complex projects from idea to production
• Self‑starter with a willingness to take ownership of tasks and navigate ambiguity in a fast‑moving environment
• Passion for tackling challenging problems in AI model quality, speed, safety, and reliability
• 10+ years of technical experience, with at least 2 of those years as a manager and at least 4 of those years working on large-scale AI model development

Desirable

• Perplexity is seeking a Research Engineering Manager to lead the team of all-star AI researchers and engineers responsible for developing the models that drive our products
• Our team has developed some of the most advanced models for agentic research, query understanding, and other domains that require accuracy and depth
• As we expand our userbase and portfolio of product surfaces, our in-house models are increasingly critical to providing a premium, high-taste experience for the world’s most sophisticated users
• You will dive into our rich datasets of conversational and agentic queries, leveraging cutting‑edge training techniques to scale AI model performance
• Through hands-on technical and organizational leadership, you will empower your team to develop SotA models for the use cases that matter most to our business and our users
• Lead a team of researchers and engineers focused on training SotA models for Perplexity-relevant use cases, leveraging the latest supervised and reinforcement learning techniques
• Drive research and engineering efforts to develop production models through advanced model training and alignment techniques, including RL, SFT, and other approaches
• Become deeply familiar with the team’s technical stack, leading from the front through hands-on technical contributions
• Own the data, training, and eval pipelines required to train and continuously improve LLM models
• Design and iterate on model training and finetuning algorithms (e.g., preference‑based methods, reinforcement learning from human or AI feedback) through an approach that balances scientific rigor and iteration velocity
• Design evaluations and improve the production model training pipeline to reliably deliver models that lie on the Pareto frontier of speed and quality
• Work closely with engineering teams to integrate in-house models into our product and rapidly iterate based on real‑world usage
• Manage day‑to‑day execution, project planning, and prioritization for the model training team to hit ambitious quality and performance goals