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Member of Technical Staff (Software Engineer, Applied AI)

Join Perplexity AI as an Applied AI Engineer to develop cutting-edge agents for enhancing user experience.

Location
San Francisco
Compensation
Not disclosed
Level
staff
Type
full time

Posted by employer 11 months ago

First seen on Joblaze 1 week ago

Last verified on the company career page 2 hours ago

Apply at Perplexity AI → Save job Scanned from perplexity.ai

Skills & Technologies

ML LLM Python AI Flexible on stack

AI in the day-to-day

Apply state-of-the-art ML and LLM techniques to solve problems and enhance user experience.

Requirements

Experience
5+ years
Education
Bachelor's degree

Not disclosed in this posting: compensation, work arrangement, visa sponsorship.

Joblaze summary

In this role, the Applied AI Engineer at Perplexity AI focuses on designing and refining advanced AI agents that enhance user experience within Perplexity Computer. The position requires strong software engineering skills, particularly in Python, along with a deep understanding of the AI lifecycle, including data analysis and model evaluation. Ideal candidates have over five years of experience in developing AI products and thrive in collaborative, fast-paced environments. This team is dedicated to pushing the boundaries of machine learning and AI, making a significant impact on user engagement.

Joblaze insights

Quick facts

How much experience is required?
At least 5 years of relevant experience for this Member of Technical Staff (Software Engineer, Applied AI) role.
What's the tech stack?
Joblaze extracted these technologies from the posting: AI, LLM, ML, Python.
What seniority level is this role?
Perplexity AI targets staff-level candidates for this position.
Is this full-time or contract?
Full-time for this Member of Technical Staff (Software Engineer, Applied AI) role at Perplexity AI.

From the original posting

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).