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Perplexity is hiring a Member of Technical Staff (Applied AI Engineer) to join the Agent Capabilities team, which sits at the intersection of frontier AI research and product innovation. This role focuses on building the foundations that enable users and agents to solve increasingly complex tasks.
You will evaluate frontier AI models against real user tasks, identify useful behaviors and failure modes, and transform the most promising advances into production agent systems. You'll own the complete lifecycle from rapid prototyping and evaluation through launch, monitoring, and iteration.
Key responsibilities include:
- Improving agents' ability to plan, use tools, manage context, recover from errors, and complete long-running tasks reliably
- Applying state-of-the-art ML and LLM techniques to design scalable agent capabilities such as skills, plugins, artifact generation, tool integration, auto-research, and multi-agent collaboration
- Owning agent behavior and capabilities end-to-end, from user-facing products to backend services
- Defining offline and online evaluations for task completion, correctness, safety, latency, cost, and user satisfaction
- Building secure, observable, and reliable agent systems with appropriate permissions and safeguards
- Developing tracing, replay, and monitoring infrastructure to make agent failures reproducible and actionable
- Collaborating with PM, Data Science, and Research teams to identify high-impact opportunities
- Setting technical direction on ambiguous problems and raising the bar through design reviews and mentorship
Tech stack includes Python, Go, Rust, PostgreSQL, DynamoDB, AWS, and TypeScript.
QUALIFICATIONS:
- Typically 6+ years of professional software engineering experience with a track record of building and owning robust AI-powered, large-scale, user-facing or data-intensive products. Exceptional candidates with less experience and an outstanding record of impact are encouraged to apply.
- Strong software engineering fundamentals with experience building and operating AI/ML products, backend services, or distributed systems at scale
- Experience owning the AI product lifecycle, including data analysis, rigorous evaluation, production monitoring, and iterative improvement
- Ability to define metrics and use production data and user feedback to guide decisions
- Practical experience in one or more relevant areas: agent harnesses, tool use, context engineering, model evaluation, browser automation, or long-running task execution
- Strong product judgment and execution; ability to translate ambiguous user needs into applied AI/ML problems and ship durable solutions with measurable user impact
- Genuine interest in frontier AI capabilities, agent systems, and excitement for rapidly exploring, evaluating, and productizing new model behaviors
NICE TO HAVE:
- Experience with LLM context engineering, harness engineering, subagents, or coding assistants
- Deep familiarity with current model families' strengths and limitations across reasoning, tool use, context management, and long-horizon tasks
- Experience building agent permissions, safeguards, evaluation infrastructure, or production observability systems
- Experience with mid-training, post-training, or reinforcement learning for frontier or open-source models
- AI/ML research experience demonstrated through publications, open-source contributions, or meaningful research impact
- Time spent at a fast-growing startup or on a high-ownership engineering team
About Perplexity AI
AI / Data / Infrastructure — AI answer engine and search product for consumers and enterprises.
