12 ΡΠ°ΡΠΎΠ² Π½Π°Π·Π°Π΄
Member of Technical Staff - Agent Engineer (AI)
200Β 000 - 300Β 000$
ΠΡΡΡ & Π‘ΠΎΠΏΡΠΎΠ²ΠΎΠ΄
ΠΠ»Ρ ΠΌΡΡΡΠ° Ρ ΡΡΠΎΠΉ Π²Π°ΠΊΠ°Π½ΡΠΈΠ΅ΠΉ Π½ΡΠΆΠ΅Π½ Plus
ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π°ΠΊΠ°Π½ΡΠΈΠΈ
Π’Π΅ΠΊΡΡ:
TL;DR
Member of Technical Staff - Agent Engineer (AI): Building and evaluating production AI-agent systems for biomedical discovery with an accent on agent harnesses, tool use, planning, memory, and multi-agent coordination. Focus on designing rigorous evaluations, improving reliability and performance, and operating long-running scientific workflows with safe execution and subagents.
Location: South San Francisco, United States; on-site
Salary: $200,000β$300,000 per year plus equity
Company
is an applied research lab building agentic intelligence to accelerate biomedical discovery.
What you will do
- Advance the agent harness by productionizing research and open-source developments in multi-agent coordination, model routing, memory, planning, and tool use.
- Build evaluations that measure agent quality, reliability, latency, and cost on representative scientific tasks.
- Monitor production agent quality, troubleshoot failures, and translate findings into engineering improvements.
- Collaborate on infrastructure for long-running sessions, sandboxed execution, background work, and subagents.
- Partner with scientists and engineers to evaluate and productionize new agent capabilities.
Requirements
- Strong software engineering experience with production backend systems, infrastructure, or distributed systems.
- Research or production experience in machine learning or another quantitative discipline.
- Familiarity with LLM APIs, tool calling, agent runtimes, or workflow orchestration.
- Strong quantitative judgment and the ability to assess whether improvements are real, reproducible, and meaningful.
- Ability to move between research questions, data analysis, system design, and production implementation.
- Experience or strong interest in AI for science, scientific agents, computational research, or automated scientific discovery.
Nice to have
- Experience with LLM evaluations, human evaluation, model judges, replay testing, benchmark design, or experiment tracking.
- Experience applying classical machine learning methods alongside LLMs in production systems.
- Experience with task queues, event streams, Kubernetes, code sandboxes, or durable workflow systems.
- Advanced degree or equivalent experience in machine learning, computational biology, physics, applied mathematics, statistics, or a related field.
Culture & Benefits
- Equity participation in building biomedical discovery technology.
- Full medical, dental, and vision coverage, including therapy sessions and an eyewear stipend.
- 401(k) and unlimited PTO for US employees.
- Lunch and snacks in the office, regular team offsites, and company events.
- Fast-paced culture focused on excellence, speed, and collaboration.
ΠΡΠ΄ΡΡΠ΅ ΠΎΡΡΠΎΡΠΎΠΆΠ½Ρ: Π΅ΡΠ»ΠΈ ΡΠ°Π±ΠΎΡΠΎΠ΄Π°ΡΠ΅Π»Ρ ΠΏΡΠΎΡΠΈΡ Π²ΠΎΠΉΡΠΈ Π² ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ iCloud/Google, ΠΏΡΠΈΡΠ»Π°ΡΡ ΠΊΠΎΠ΄/ΠΏΠ°ΡΠΎΠ»Ρ, Π·Π°ΠΏΡΡΡΠΈΡΡ ΠΊΠΎΠ΄/ΠΠ, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡΠ΅ ΡΡΠΎΠ³ΠΎ - ΡΡΠΎ ΠΌΠΎΡΠ΅Π½Π½ΠΈΠΊΠΈ. ΠΠ±ΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΠΆΠΌΠΈΡΠ΅ "ΠΠΎΠΆΠ°Π»ΠΎΠ²Π°ΡΡΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡΠΈΡΠ΅ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ. ΠΠΎΠ΄ΡΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β
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