Platform Engineer

3 days ago

San Francisco, California, United States Jecona Full time $120,000 - $220,000 per year

We are partnering with a mobile start up that is building the next generation of dating — one that's more honest, intentional, and human. They are looking for a Platform Engineer that wants to make decisions that influence how the product scales, performs, and evolves — from the data stack to the deployment layer

This company cannot sponsor, unfortunately, and is an onsite role in the Marina District - they may be open to hybrid with 3 days/week and we can talk about that in our initial call. The max salary is 220k, depending on experience.

What you'll do:

  • Design and manage cloud infrastructure (AWS-first, with IaC via Terraform).
  • Establish CI/CD pipelines and best practices for rapid, safe iteration (GitHub Actions, Docker, Kubernetes, etc.).
  • Build and maintain scalable data ingestion and orchestration pipelines to support ML and product analytics.
  • Administer and optimize our databases — PostgreSQL (with pgvector for embeddings) and analytical warehouse.
  • Collaborate with AI/ML engineers to deploy and monitor LLM and matching models for inference, evaluation, and retraining.
  • Implement observability (logging, metrics, traces, alerts) across backend services, data jobs, and model endpoints.
  • Drive reliability and scalability across our web, mobile, and agentic systems — from real-time voice matching to background batch workflows.

What you bring:

  • 4+ years of experience in infrastructure, platform, or data engineering (startup or high-growth environments preferred).
  • Strong proficiency in Python, TypeScript, and scripting (Bash/YAML).
  • Deep understanding of cloud architecture (AWS, GCP, or similar) and Infrastructure-as-Code (Terraform, Pulumi, or CloudFormation).
  • Solid experience with containerization and orchestration (Docker, Kubernetes, ECS).
  • Proven ability to design and operate data pipelines and distributed systems.
  • Experience with PostgreSQL (ideally with pgvector or embeddings), data modeling, and schema design for real-time and analytical workloads.
  • Familiarity with ML/AI workflows (model training, inference, monitoring) and feature stores is a plus.
  • DevOps fundamentals: observability, cost optimization, and security.
  • Collaborative mindset, strong ownership, and bias toward shipping working systems fast

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