Perplexity
Member of Technical Staff (Applied AI Engineer, Agent Capabilities)
$220K–$405K
San Francisco +1 · Staff · Full-time · Posted Sep 10, 2026
- Location
- San Francisco
New York City - Experience
- Typically 6+ years of professional software engineering experience; exceptional candidates with less experience and an outstanding record of impact are encouraged to apply.
About the job
Build and productize reliable agent capabilities by evaluating frontier models and translating useful behaviors into scalable, user-facing AI products.
What you'll do
- Evaluate frontier models and own agent features from prototyping and evaluation through launch, monitoring, and iteration.
- Improve agents’ planning, tool use, context management, error recovery, and execution of long-running tasks.
- Build and operate secure, observable agent systems and evaluation infrastructure; define measures of quality, safety, latency, cost, and user satisfaction.
What they ask for
- Strong software engineering fundamentals and experience building and operating AI/ML products, backend services, or distributed systems at scale.
- Experience owning the AI product lifecycle, including data analysis, evaluation, production monitoring, and iterative improvement.
- Practical experience in one or more relevant areas, such as agent harnesses, tool use, context engineering, model evaluation, browser automation, or long-running task execution.
- Strong product judgment and ability to turn ambiguous user needs into durable applied AI/ML solutions with measurable impact.
Nice to have
- Experience with LLM context or harness engineering, subagents, coding assistants, or autonomous task execution.
- Experience with agent safeguards, evaluation or observability systems, model training or reinforcement learning, or AI/ML research.
Pay
$220K – $405K USD base salary
Tools
Python, Go, Rust, PostgreSQL, DynamoDB, AWS, TypeScript
AI focus
Evaluate and apply frontier AI and LLM capabilities to production agent systems, including tool use, planning, context management, multi-agent collaboration, evaluation, safeguards, and reliable long-running tasks.
Model-written summary · the original posting is authoritative