Senior Full-Stack Developer(Python)

Senior Full-Stack Engineer (Python-first) with strong back-end depth and solid front-end

fluency. You’ll take ownership of core services and distributed evaluation pipelines, harden

them for scale and reliability, and help guide architecture as we grow from “works at scale”

to “enterprise-grade.” This is an IC role with a clear path to team lead as we expand.

Key Responsibilities
 

•  Design, build, and own Python microservices powering evaluation, gating, and optimization workflows
• Extend distributed compute for large-scale evaluations using Ray/Optuna (or similar)
• Architect cloud-native data/optimization pipelines with strong SLAs and fault tolerance
• Define and maintain clean APIs and event/async communication across services
• Drive performance, observability, tracing, and reliability (metrics, logs, alerts)
• Automate build/test/deploy with Docker, Kubernetes, and CI/CD pipelines
• Contribute to and integrate with the CLI/SDK and support light React/TypeScript UI needs
• Collaborate closely with the founder/CTO and product/design/devrel on roadmap and milestones

• Use LLM-assisted tooling to accelerate coding, reviews, documentation, and tests
  
  Required Skills & Experience

   

• 6+ years in software engineering with strong Python back-end experience
• Proven track record with distributed systems and microservices (async/event-driven)
• Hands-on with Ray, Optuna, or comparable distributed/optimization frameworks
• Strong API design, data pipeline design, and service-to-service integration skills
• Production experience on AWS/GCP/Azure; solid Kubernetes + Docker + CI/CD
• Working knowledge of React/TypeScript/Next.js for light full-stack tasks
• Obsessed with quality: testing, automation, code health, and continuous improvement
• Excellent communication in English; self-driven and comfortable in a fast-moving startup
  
  Nice to Have
• Experience with evaluation-driven or LLM-assisted development workflows
• Background in distributed optimization, experiment design, or scheduling at scale
• Deep observability chops (tracing, profiling, cost/latency budgeting)
• DevOps/SRE familiarity (infra-as-code, security hardening, multi-tenant SaaS)
• Data/ML plumbing (feature stores, vector DBs, retrieval)

• Contributions to developer tooling (CLI/SDKs) or platform enablement
   

  Why join now

  You’ll define the technical backbone of a new category—Evaluation-Driven AI Engineering—and ship a platform that lets teams evaluate → gate → optimize every AI decision, not just tweak parameters. If you love turning deep research into everyday engineering practice, let’s talk.