Fluency
Software Engineer, AI Platform
San Francisco · Exec · Full-time
Sponsorship not specified$180k-$250kDetected 67 days ago
TypeScriptPythonNode.jsFastAPIPostgreSQLVector DatabasesAWSTerraformDatadogTemporalPandasSparkAirflowdbtData EngineeringLLMsLLMOps
About the role
- It moves data through LLMs, transforms agent outputs into structured downstream data, runs jobs reliably, and keeps the system fast, cheap, and observable as we scale.
- This is an in-person role, 5 days a week in our office.
Responsibilities
- Own the data platform: Maintain and evolve the platform that powers every job across the company.
- Build agent transformation infrastructure: The systems that take agent outputs and turn them into structured, queryable data downstream.
- Build observability and tooling so the team can debug and iterate quickly.
- Partner with AI Engineers: Expose new capabilities through the platform and shape the interfaces they build on.
- Experience building or maintaining production pipelines that handle non-trivial volume, retries, backfills, and failure recovery
- Comfort with PostgreSQL at scale: schema design, multi-schema setups, and migrations
- Maintain and evolve the platform that powers every job across the company.
- Expose new capabilities through the platform and shape the interfaces they build on.
Requirements
- The ability to balance reliability with iteration speed is essential.
- Strong Python engineering experience supporting production systems (FastAPI or similar)
- Hands-on experience with a data orchestrator (Dagster, Airflow, Prefect, or Temporal) and dbt or similar transformation tooling
- Comfort with AWS infrastructure (ECS, Lambda, SQS, Step Functions, RDS, S3) and IaC (Terraform / Terragrunt)
- Familiarity with LLM APIs and the operational realities of LLM-based systems (latency, cost, retries, structured output, failure modes)
- Requirements need to be locked down before you can move
Nice to have
- Experience with distributed compute for Python workloads: Anyscale Ray, Dask, or Spark
- Experience with Polars and Pandas for data processing
- Familiarity with Datadog for observability, metrics, and tracing
- Cost optimization experience for LLM workloads
- Familiarity with pgvector or other vector stores
- Multi-region AWS deployment experience
- Some TypeScript/Node experience, since parts of the platform live there
- We capture observable work data across tools and systems, structure it into a model of how work runs, and use it to measure productivity, check process conformance, and analyze where AI changes the work.
Compensation
- US$1,000 per month food and commuting allowance
- US$180,000 to US$250,000
Benefits
- We offer E-3 sponsorship for Australians to relocate with stipend.
Visa & Work Authorization
- We offer E-3 sponsorship for Australians to relocate with stipend.
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This listing is sourced directly from Fluency's careers page and normalized into a canonical job model.