Software Engineer, AI & Data

The Role

We're hiring a mid-level software engineer with strong TypeScript and Python, data engineering and
applied LLM skills. You'll build backend services, data pipelines and LLM-powered features, then own them in production. This role suits a generalist who is comfortable moving between these areas and wants to go deep in at least one.

What You’ll Do

• Build and maintain TypeScript and Python backend services and REST APIs.
• Design batch data pipelines with orchestration, tests and data quality checks.
• Build LLM features end to end: prompts, retrieval, structured outputs and evaluation, tracking
accuracy, latency and cost.
• Turn scanned documents into structured data using OCR and LLMs.
• Containerize, deploy and monitor services in the cloud, and share on-call [rotation details].
• Write tests, review code and document what you build.
• Work with product, operations and data partners to scope work and ship in small, frequent
increments.

What We’re Looking For

• 3 to 5 years of professional software engineering experience, or equivalent.
• Production experience with the Required technologies in the tech stack above.
• Has shipped at least one LLM-powered feature to real users, with a way to measure its quality.
• Clear written communication, including explaining technical tradeoffs to non engineers.

Nice to Have
Experience with an AI-driven SDLC: using coding agents and assistants (e.g., Claude Code, Cursor or GitHub Copilot) across planning, coding, testing and code review.
Experience handling sensitive data (PII, financial or health records) under compliance programs like SOC 2.
Small-team experience owning systems end to end, from design through on-call.
Technology Stack
Strength in the required technologies matters most. Nice-to-have technologies are a plus, not a filter.

Required

Languages: TypeScript and Python (strong), SQL (solid)

Backend and APIs: REST APIs in Node.js or Python (e.g., Express, NestJS, FastAPI or Django); background jobs and queues

Cloud and infrastructure: AWS, GCP or Azure; Docker; CI/CD

Data engineering: Batch pipelines with Airflow, Dagster or dbt; data quality testing

Applied AI and LLMs: LLM APIs (OpenAI, Anthropic or similar); prompt design; measuring output quality

Observability: Logging, metrics and alerting for production services

Nice to Have

Backend and APIs: Event-driven systems (Kafka, SQS or Pub/Sub)

Cloud and infrastructure: Kubernetes, Terraform, Pulumi

Data engineering: Snowflake, BigQuery or Redshift; Metabase

Applied AI and LLMs: RAG and vector search (pgvector, Pinecone); OCR and document AI (Textract, Google Document AI, Azure Document Intelligence)

Observability: Sentry, PostHog or Datadog