Future - Staff Applied AI Engineer
At a glance
Senior AI Engineering role at deCircle. Remote, United States · no salary stated · we estimate $203,250–$283,250.
deCircle has not published a salary for this role. The figure below is our estimate, not theirs.
remoteroles summary, based on the employer's posting.
What you'll do
- Build and launch Health Coach agents that call tools, return structured results and coordinate multiple APIs
- Create streaming features for coach matching and guidance on sleep, nutrition and fitness, with human review in the loop
- Run the agent development cycle from prototypes and Langfuse evaluations through deployment, trace monitoring and iteration
- Make agents faster, more reliable and less costly using caching, token limits, retries and observability
- Develop Pydantic-validated integrations with an EPD pod
What you bring
- Four to ten years of experience, including taking LLM agents into production
- Strong Python and asynchronous programming skills, plus experience with LangChain or LangGraph
- Hands-on use of Pydantic, Langfuse and AWS or Bedrock
- U.S. citizenship or permanent resident status
Who this fits
This senior role suits an engineer who can work independently in a beta-stage environment. It is fully remote within U.S. time zones, with optional anchor weeks at the Brentwood, Los Angeles headquarters. Visa sponsorship is not available.
From the employer
Future (future.co): personal fitness/health coaching; building the AI foundation of a new Health Coach product. $110M raised; board includes Tom Brady, Eddie Cue, Adam Bain.
Comp: $250K-$300K base (SF rates for all US) + competitive equity. Location: Fully remote, US time zones; optional anchor weeks at Brentwood LA HQ. Visa: Not open to visas (US citizen / Green Card).
Responsibilities
Build and ship production AI agents for Health Coach: tool-calling LLMs, structured output, parallel API orchestration, streaming
AI features: coach matching, clinical-leaning guidance (sleep, nutrition, fitness), human in the loop
Own the loop: prototype, Langfuse evals/rubrics, deploy, monitor traces, iterate
Improve reliability/latency/cost: prompt caching, token budgets, retries, observability
Write agent tools (Pydantic-validated integrations) in an EPD pod
Tech stack: Python, Async Python, LangChain, LangGraph, LLMs, Pydantic, JSON Schema, SSE, Webhooks, Langfuse, AWS, AWS Bedrock, OpenTelemetry, Datadog, Terraform, CDK
Source: https://www.paraform.com/share/future/cmur5lxlk000g06nis011p9o1
4-10 yrs; shipped LLM agents to production
Strong big-tech background, ideally moved to startups or founded a company
Python/async, LangChain/LangGraph, Pydantic, Langfuse, AWS/Bedrock
Independent in a beta-stage environment; US citizen/GC
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