Member of Technical Staff - Engineering Lead, Compute Platform

Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.

Reflection is building the trusted data backbone for the world's most capable open-weight AI systems. The Data Platform team builds and operates the core data systems and pipelines that power our research, training, and production environments unifying ingestion, processing, and orchestration across the entire data lifecycle so every team can move faster. We're looking for a front-line technical leader to build, mentor, and grow this team. You'll guide its technical direction while staying hands-on this is a role for someone who has earned deep technical credibility as an engineer and now multiplies it through a team. You'll work closely with our research team to understand what high-velocity experimentation actually needs, and turn that into reliable, reproducible, scalable data infrastructure.

Build, mentor, and grow a team of ~10 exceptional data platform engineers hiring, coaching, and raising the bar with every person you add. Guide the technical direction across the platform: ingestion and orchestration patterns for batch and streaming workloads, scalable compute and storage foundations, and reproducible pipelines with versioning, backfills, and isolated execution environments. Work closely with research, training, and production teams to enable high-velocity experimentation on a unified data layer. Establish trusted data quality, lineage, and governance signals so teams can make confident production decisions. Keep cost and performance predictable through guardrails, budgets, and continuous system tuning. Stay hands-on: design reviews, architecture decisions, and code where it matters most. What you'll work with Compute & orchestration: Spark, Flink, Beam, Airflow, Dagster, Kafka, PubSub Storage & analytics: data lake and warehouse architectures, Parquet, Iceberg, Delta Lake, BigQuery, Snowflake Metadata & data quality: lineage systems, metadata management, Great Expectations, reproducibility systems Cost & performance: partitioning strategies, clustering, cost optimization, SLA-driven pipelines About you 8+ years of engineering experience with a strong data engineering foundation — you've shipped and owned production-grade pipelines at large scale (tens of TBs to PBs daily). A track record as a staff/principal-level individual contributor before moving into leadership — you earn the technical trust of a strong team. You've managed and grown a team (~5–10), or clearly demonstrated the leadership readiness to build one quickly — while staying technically embedded. Deep in at least one of: ingestion & orchestration at scale, storage and processing engines (lakehouse formats, query engines), or data quality/lineage/reproducibility systems — with working breadth across the others. Fluent in the modern data stack; you've built new systems from zero rather than maintained legacy ones. Thrive in a high-agency, fast-paced environment; bias toward action and impact. Collaborative, clear communicator, comfortable working across research and infrastructure boundaries.

We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock