Member of Technical Staff

Function Health; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

Function Health, a health AI platform, is hiring a Senior Applied AI Engineer to own production multi-agent systems, from orchestration to memory and retrieval across health data. You’ll wire frontier LLMs into proprietary data with strong safety, latency, and cost controls.

You’ll collaborate with clinicians and engineers to translate research into robust production systems, balancing speed with reliability and governance in a HIPAA-conscious environment.

Qualifications

• 1+ years building agentic AI systems; 6+ years as a full-stack or ML engineer
• Fluency with agentic orchestration and tool calling
• Experience integrating frontier LLMs
• Strong with API design and backend frameworks
• Data systems expertise with PostgreSQL
• Retrieval and memory with vector databases and graph storage
• Production evals and CI-integrated tests
• Observability and SRE practices
• Cloud-native delivery with Kubernetes/DevOps

Responsibilities

• Architect and build stateful, graph-based agent workflows with tool use, planning, and memory
• Integrate LLMs and multimodal models via structured I/O and function/tool calls
• Build high-reliability APIs and streaming services for real-time inference
• Own production readiness: tracing, logging, metrics, rate limiting, and SLOs
• Stand up eval pipelines: offline and online evaluations in CI
• Implement retrieval and memory: vector/graph retrieval, memory stores
• Optimize cost/latency: model routing and caching
• Translate research into robust production systems and iterate quickly
• Mentor engineers through design docs and architecture decisions

Skills

Agentic AI systems

Full-stack or ML engineering

Python/Go

LangGraph/PydanticAI/DSPy

API design (FastAPI/Flask)

PostgreSQL

Vector databases

LLM evaluation/CI tests

Observability/SRE

Cloud-native (Kubernetes, Terraform)

GitHub Actions

Tools

LangGraph

PydanticAI

DSPy

LlamaIndex

pgvector

Pinecone

Weaviate

Milvus

Job description

Senior Applied AI Engineer

US - Remote • Canada - Remote

Engineering

Remote

Full-time

Function Health is the AI operating system for health, designed to empower people to live 100 healthy years. We are redefining how individuals understand, measure, and improve their health by moving beyond reactive care and enabling proactive, data-driven insight into human biology. Function has been recognized as one of Fast Company’s Most Innovative Companies of 2024, and is venture-backed by Andreessen Horowitz (a16z). Hundreds of thousands of members have joined Function to take control of their health.

Through advanced diagnostics, deep biomarker testing, longitudinal data, and AI-enabled insights, Function equips members with actionable intelligence to take control of both the quality and length of their lives.

Function recently announced a $298M Series B and is entering its next chapter of growth. As we scale, the quality and durability of our People systems, data, and insights will directly shape our ability to attract, retain, and support exceptional talent.

We are growing our team and seeking out world-class talent that deeply believes in our mission to positively impact global health, has a relentless bias toward action, and a growth mindset. Function fosters a collaborative and dynamic environment where every day we build the future.

We sit on something rare: a fast-growing longitudinal health dataset spanning 50M+ lab tests, 160+ biomarkers per member, advanced imaging, and wearable signals, captured continuously rather than once a year at a checkup.

You'll own the agentic system that turns a member's health data into clear next steps, working alongside their clinicians. Day to day, that means building and operating production multi-agent systems end to end: orchestration graphs, tool use and memory, retrieval over a member's records, and the evals and observability to keep it reliable. You'll wire frontier LLMs into Function's proprietary data, and hold the whole thing to the safety, latency, and cost controls healthcare demands.

This is a hands-on, high-ownership role for an engineer who has built agents at scale and wants their work measured in healthy years of human life, not just benchmarks.

Impact You'll Drive:

• Put a concierge doctor in every member's pocket: an agent that reasons over a lifetime of labs, imaging, and wearable signals and tells them what to do next.
• Turn our longitudinal health dataset into decisions, not dashboards, so members act on their health rather than just view it.
• Make health AI that people trust with their lives: every recommendation earns its way past evaluation gates and clinician review before it reaches a member.
• Catch what a once-a-year physical misses, surfacing the early signal across 160+ biomarkers before it becomes a diagnosis.

Key Responsibilities:

• Architect and build stateful, graph-based agent workflows with tool use, planning, and memory.
• Integrate LLMs and multimodal models via structured I/O (JSON Schema, Pydantic validators) and function/tool calling.
• Build high-reliability APIs and streaming services for real-time inference, speech, and vision.
• Own production readiness: tracing, logging, metrics, rate limiting, circuit breakers, and SLOs.
• Stand up eval pipelines: offline golden sets, LLM-as-judge with human rubrics, online A/B, and regression tests in CI.
• Implement retrieval and memory: hybrid search, vector and graph retrieval, semantic caches, and long-horizon context.
• Optimize cost/latency: model routing, prompt and tool selection, quantization, and KV cache/prefill strategies.
• Partner cross-functionally to translate research into robust production systems and iterate quickly behind evaluation gates.
• Mentor engineers through design docs and architecture decisions.

Qualifications/Skills:

To excel in this role, candidates must bring a unique combination of skills and experiences:

• 1+ years building agentic AI systems; 6+ years as a full-stack or ML engineer, building production backends or ML systems in Python, Go, or similar.
• Fluency with agentic orchestration (e.g., LangGraph, PydanticAI, DSPy, LlamaIndex), and tool/function calling.
• Experience integrating frontier LLMs and multimodal models via managed APIs or self-hosted serving.
• Strong with API design and backend frameworks (FastAPI, Flask) and event-driven architectures.
• Data systems expertise with PostgreSQL, including token streaming and throughput tuning.
• Retrieval and memory: vector databases (pgvector, Pinecone, Weaviate, Milvus), hybrid search, and graph/knowledge storage.
• Production evals: LLM-as-judge, human-in-the-loop, rubric design, and CI-integrated regression tests.
• Observability and SRE: OpenTelemetry traces, metrics, structured logs, SLOs, dashboards, and on-call triage.
• Cloud-native delivery: Kubernetes, Terraform, Docker, GPU scheduling/autoscaling on AWS or GCP.
• CI/CD proficiency with GitHub Actions and test automation for prompts, tools, and agents.
• Clear, concise communication and high ownership in fast-paced environments.

Nice to Haves:

• Real-time multimodal systems: streaming ASR, low-latency TTS, WebRTC, and vision pipelines.
• RAG expertise beyond basics: Graph RAG, multi-hop retrieval, sub-agents, query planning, and freshness policies.
• Safety and governance: policy-as-code, red-team, PII handling, audit logs, and role-based tool authorization.
• Regulated data experience (HIPAA, SOC 2, GDPR) and data residency controls.
• Personalization at inference time, long-term memory agents, session state, and episodic memory stores.
• Experience with consumer-scale AI apps, high-traffic systems, or on-device/edge acceleration (WebGPU).

To be a strong fit, you also need:

• Ruthless Prioritization:
  • We don't let perfect get in the way of progress.
  • We move quickly to drive value, not perfection.
  • We prioritize what drives impact.
  • We never compromise on standards of excellence.
• Member-First, Always:
  • We design and deliver like we're caring for someone we love.
  • We