Own backend features end-to-end: discovery, design, implementation, rollout, and ongoing reliability and operations, with support from more experienced teammates as needed.

Help design and evolve distributed systems (services, pipelines, and data stores) with an eye toward performance, scalability, and resiliency.

Build and maintain APIs and data access patterns that support analytics and search use cases.

Develop, maintain, and optimize scalable data pipelines that power product features, analytics, and machine learning workloads.

Ensure data reliability, quality, and performance across our systems, and monitor and troubleshoot pipelines to ensure consistent, timely delivery.

Build strong engineering habits: thoughtful code reviews, solid testing, incident readiness, and operational excellence.

Apply an experimentation-first approach: define hypotheses and success metrics/guardrails, run controlled rollouts and A/B tests when appropriate, and write clear readouts for stakeholders.

Use AI coding tools like Claude Code productively and responsibly as part of your development workflow - for implementation, debugging, refactoring, and design reviews - while maintaining high standards for correctness, security, and privacy.

Bring evaluation discipline to AI-assisted work: treat prompts and configs like versioned artifacts, design regression tests, measure quality changes, and monitor for drift the same way you would for performance or correctness.

Grow continuously: actively seek feedback, learn new tools, languages, and domains quickly, and apply what you learn to your work.

Collaborate with Product Managers and fellow Engineers to ship intelligent, data-driven products.

Share knowledge with teammates through clear documentation, pairing, and participation in code reviews.

Document systems, pipelines, and architecture, and help evolve our engineering best practices.

Stay current with emerging tools, frameworks, and trends across software, data, and AI engineering.

A few years of professional software engineering experience building backend systems, and a desire to grow into larger distributed systems challenges.

A growth mindset: curiosity, a habit of learning new tools and domains quickly, and openness to feedback.

Strong general-purpose programming skills and software engineering fundamentals.

Solid debugging skills and the ability to troubleshoot and performance-tune production services.

Strong SQL and data modeling skills.

Experience with version control (Git) and CI/CD workflows.

Comfort using AI coding assistants like Claude Code as part of your workflow, and the discipline to validate outputs (tests, metrics, evaluation) rather than trusting them blindly.

Strong problem-solving and communication skills, and the ability to collaborate across functions.

Experience building and maintaining data pipelines (ETL/ELT).

Exposure to event-driven architectures, cloud deployment on AWS, and containers (Docker/Kubernetes).

Hands-on experience building or deploying AI/ML-powered features or data-driven products.

Familiarity with machine learning workflows, including data preparation, training, and deployment.

Familiarity with ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch, or similar).

Exposure to LLMs, NLP, or generative AI use cases.

Experience with Databricks, Apache Spark, or similar distributed data platforms (including cost monitoring and optimization).

Experience deploying ML models using MLOps tools (e.g., MLflow, Airflow, Kubeflow).

Experience with workflow/orchestration tools (Airflow, Argo, Dagster), Terraform/Ansible, and Grafana dashboards.

Search/retrieval systems (Elasticsearch/Lucene) and GraphQL.

Understanding of real-time or streaming data pipelines.