Member of Technical Staff (Software Engineer, Applied AI)

Fdetimes; San Francisco (CA); In loco; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

Perplexity is seeking an Applied AI Engineer to design, build, and iterate on cutting-edge agents powering our Perplexity Computer experience in San Francisco. You will own the model lifecycle from research to production, including data analysis, evaluation, offline/online testing, and continuous improvement, collaborating with engineers, PMs, data scientists, and designers.

Strong Python software engineering skills and a track record shipping AI products at scale are essential, with a passion

Competenze

• 5+ years of experience building and shipping robust AI products.
• Strong software engineering skills in Python and production-quality codebases.
• Experience across the full AI lifecycle: data analysis, evaluation, and monitoring.
• Proven collaborator in high-velocity, cross-functional teams.
• Curious and driven by end-user/product impact and advancing applied ML/AI.
• BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent).

Mansioni

• Apply state-of-the-art ML and LLM techniques to personalize experiences and solve problems.
• Develop frontier context layer applications on top of Perplexity Computer.
• Own the entire model lifecycle from research to production, including A/B testing.
• Collaborate with engineers, PMs, data scientists, and designers to drive product improvements.
• Stay at the forefront by evaluating and incorporating emerging research and algorithms.

Conoscenze

AI product development

Python programming

Cross-functional collaboration

End-to-end AI lifecycle

User impact

Advanced ML techniques

Formazione

BS/MS/PhD in Computer Science or related field

Strumenti

Python

Descrizione del lavoro

Perplexity is looking for an Applied AI Engineer to design, build, and iterate on cutting-edge agents powering our core experience in Perplexity Computer. Working in this mission critical team, you will develop frontier context layer applications - fulfilling the curiosity of millions of users across the globe.

Key Responsibilities

• Apply state-of-the-art ML and LLM techniques to solve problems spanning:
• Personalization (LLM memory, context summarization, retrieval and ranking);
• Contextual recommendations and Monetization applications
• Build frontier agent capabilities on top of Perplexity Computer
• Build auto research harness for both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact.
• Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement and build autonomous harness for agent squad to explore different problem spaces.
• Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements.
• Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle.

Preferred Qualifications

• 5+ years experience building and shipping robust AI products for large-scale, user-facing or data-driven products.
• Strong software engineering skills (Python, production-quality codebases, collaborative development) and experience using agentic coding tools for large scale parallel developments.
• In-depth experience with the full AI lifecycle: data analysis, rigorous evaluation, and ongoing monitoring/improvement.
• Proven collaborator and communicator; excels in high-velocity, cross-functional teams.
• Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI.
• BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience).

Bonus Points For

• Experience with LLM context engineering or harness engineering.
• Experience in mid-training or post-training frontier open source models
• Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc).