USAMember of Technical Staff - Data Platform
1 мес. назад
USALeadOnsite
data pipelinesstreamingbatch processingdata ingestiondata governancescalable computedata storage
Build and operate core data systems and pipelines to support research, training, and production environments with scalable data processing infrastructure.
О компании
- 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.
Обязанности
- Build the core data systems and pipelines that power our research, training, and production environments. Design and implement reliable, scalable ingestion and orchestration patterns for batch and streaming workloads. Develop the storage and compute foundations that enable reproducible experimentation and high-velocity iteration. Drive data quality and governance standards that teams can trust for production decisions. Provide the foundational data layer that unifies ingestion, processing, and workflow management across model development.
Требования
- Strong data engineering background with experience shipping production-grade pipelines at scale.
- Experience designing and owning end-to-end data systems handling large-scale batch and streaming workloads.
- Comfortable debugging complex pipeline failures, optimizing for cost and performance, and maintaining data quality.
- Thrive in a high-agency, fast-paced environment; bias toward action and impact.
- Excited about zero to one challenges — building new systems rather than maintaining legacy ones.
- Collaborative, clear communicator, comfortable working across research and infrastructure boundaries.
- Motivated by creating the trusted data backbone for the world's most capable open-weight AI systems.
Условия
- 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 options.
- Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.
- Meals: Lunch and dinner are provided in the office daily.
- Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
- Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.
- Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
- Team building: We have regular off-sites, happy hours, and team celebrations.
- Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization.
Другое
- Build and operate a company-wide foundations platform that accelerates every team by providing reliable, scalable developer infrastructure, SRE capabilities, and high-throughput data ingestion tooling enabling Reflection to move faster as we scale.
- Build and operate the core data systems and pipelines that power our research, training, and production environments. This platform enables high-velocity experimentation, reliable model development, and scalable production workflows by unifying ingestion, processing, and orchestration across the data lifecycle.
- Design ingestion and orchestration patterns for both batch and streaming workloads.
- Build scalable compute and storage foundations (formats, engines, runtimes) that support large-scale data processing.
- Ensure reproducible pipelines through versioning, backfills, and isolated execution environments.
- Provide trusted data quality, lineage, and governance signals so teams can make confident production decisions.
- Maintain predictable cost and performance through guardrails, budgets, and continuous system tuning.
- Enable a unified data layer that supports research, training, and production across the model development lifecycle.
- 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 Management: Partitioning strategies, clustering, cost optimization, SLA-driven pipelines.