Tech Lead, Data & Inference Engineer

Tech Lead, Data & Inference Engineer – Catalyst Labs

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A fast‑moving, venture‑backed advertising technology startup based in San Francisco. They have raised twelve million dollars in funding and are transforming how business‑to‑business marketers reach their ideal customers. Their identity resolution technology blends business and consumer signals to convert static audience lists into high match and cross‑channel segments without the use of cookies. By transforming first‑party and third‑party data into precision‑targetable audiences across platforms such as Meta, Google and You Tube, they enable marketing teams to reach higher match rates, reduce wasted advertising spend and accelerate pipeline growth.

Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning and Data Science. We stand out as an agency deeply embedded in our clients’ recruitment operations, collaborating directly with founders, CTOs and heads of AI to drive the next wave of applied intelligence.

San Francisco

Full Time

Above market base salary + bonus + equity

• Lead the design, development and scaling of an end‑to‑end data platform from ingestion to insights, ensuring data is fast, reliable and ready for business use.
• Build and maintain scalable batch and streaming pipelines, transforming diverse data sources and third‑party APIs into trusted and low‑latency systems.
• Take full ownership of reliability, cost and service level objectives, achieving 99.9% uptime, minute‑level latency and optimizing cost per terabyte.
• Operate inference pipelines that enhance and enrich data, including enrichment, scoring and quality assurance using large language models and retrieval‑augmented generation. Manage version control, caching and evaluation loops.
• Work across teams to deliver data as a product through creation of clear data contracts, ownership models, lifecycle processes and usage‑based decision making.
• Guide architectural decisions across the data lake and the entire pipeline stack. Document lineage, trade‑offs and reversibility while deciding on in‑house build versus external purchase.
• Scale integration with APIs and internal services, ensuring data consistency, high data quality and support for both real‑time and batch use cases.
• Mentor engineers, review code and raise the overall technical standard across teams, promoting data‑driven best practices throughout the organization.

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering or Mathematics.
• Excellent written and verbal communication; proactive and collaborative mindset.
• Comfortable in hybrid or distributed environments with strong ownership and accountability.
• Founder‑level bias for actionable insight: identify bottlenecks, automate workflows and iterate rapidly based on measurable outcomes.
• Demonstrated ability to teach, mentor and document technical decisions and schemas clearly.

• 6 to 12 years of experience building and scaling production‑grade data systems, with deep expertise in data architecture, modeling and pipeline design.
• Expert SQL (query optimization on large datasets) and Python skills.
• Hands‑on experience with distributed data technologies (Spark, Flink, Kafka) and modern orchestration tools (Airflow, Dagster, Prefect).
• Familiarity with dbt, DuckDB and the modern data stack; experience with IaC, CI/CD and observability.
• Exposure to Kubernetes and cloud infrastructure (AWS, GCP or Azure).
• Bonus:
  Strong Node.js skills for faster onboarding and system integration.
• Previous experience at a high‑growth startup (10 to 200 people) or early‑stage environment with a strong product mindset.

Not Applicable

Full‑time

Engineering and Information Technology

Business Consulting and Services