Applied AI Engineer - Agent

What you'll do

• Design agent capabilities such as prompting, tool selection, planning, memory, safety, and recovery.
• Build evaluation pipelines and experimentation frameworks to measure agent quality, reliability, latency, and cost.
• Productionize LLM techniques and improve backend systems, integrations, observability, and agent memory.

What they ask for

• 4+ years of backend engineering experience; Python preferred, with impact valued over years.
• Hands-on LLM experience and production delivery of LLM features, including prompting, function-calling, retrieval, embeddings, and evaluation design.
• Track record building evaluation harnesses and using them to drive improvements.
• Solid distributed systems fundamentals, plus debugging and instrumentation skills.

Nice to have

• Experience with agent frameworks, tool orchestration, and memory architectures.
• Production RAG experience; familiarity with Redis, Postgres/Supabase, queues, event-driven designs, or observability stacks.

Pay

$250K – $300K USD base salary; 0.3% – 0.6% equity (currency and whether this is base salary or total pay are not specified for the equity range)

Tools

Python, Redis, Postgres/Supabase, Celery/Arq/SQS, Datadog, OpenTelemetry, Gmail, Slack

AI focus

Applied LLM and agent development, including reliability, autonomy, evaluation, tool orchestration, retrieval/RAG, multi-agent handoffs, memory, and hallucination reduction.