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Member of Technical Staff (Software Engineer, Capabilities) at Perplexity · Lumi · Lumi

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Perplexity· Posted 2026-06-13

Member of Technical Staff (Software Engineer, Capabilities)

San Francisco · Remotefull timeRemote

senior

$220k–$405k

H1B: yes

Perplexity The Member of Technical Staff on the Capabilities team at Perplexity owns systems spanning the agent product, core engine, and evaluation of Skills and Artifacts. The role involves integrating new model capabilities into the product and building evaluation systems to ensure best-in-class performance.

Responsibilities

Build a deep, hands-on understanding of how frontier LLMs reason and where they break, then turn that into better engine and primitive design.
Bring each new model capability into the product as it reaches the frontier, including planning, long-running tasks, autonomous execution, and self-evolving skills and agents.
Work closely with the agent engine layer (context management, tool calling, planning, long-horizon execution) to turn frontier capabilities into reliable primitives.
Design, build, and own the primitives at the heart of Computer (Skills, Workflows, and Artifacts) so they compose into one coherent experience.
Build the evaluation systems (benchmarks, evals, rubrics, feedback loops) that make each capability best-in-class before broad rollout.
Set technical direction on ambiguous problems and raise the bar through design reviews, mentorship, and example.

Qualification

Demonstrated technical leadershipComfort with data-informed decisionsGenuine interest in AI productsExperience with evaluation

Required

8+ years of professional software engineering experience, shipping and owning complex systems end-to-end.
Strong backend / Full Stack engineering skills, with experience in designing and building scalable and reliable distributed systems, serving high traffic and a large user base.
Demonstrated technical leadership: you scope ambiguous problems, set direction, and drive cross-team projects to durable outcomes.
Strong product judgment and ownership instincts; you turn vague needs into simple, reliable systems and ship without waiting for perfect specs.
Comfort with data-informed decisions; you define the metrics and evals that prove a system works, and iterate on them.
Genuine interest in AI products, with hands-on adoption and a willingness to learn quickly.

Preferred

Experience building agentic systems (tool calling, subagents, long-running or autonomous task execution).
Experience building developer platforms or reusable-capability primitives (SDKs, plugin systems, workflow engines).
Experience with evaluation, benchmarking, or quality systems for ML/LLM-powered products.
Time spent at a fast-growing startup or on a high-ownership engineering team.