Location: Remote — Los Angeles area preferred
Employment: Full-time
Experience: 5–8 years
Compensation: $200,000–$240,000 + 0.25%–0.5% equity
Work Authorization: U.S. citizenship required
About the Role
We're looking for a Senior Software Engineer, Data Platform to build and own a data platform from the ground up. This is a highly autonomous, zero-to-one role focused on turning large volumes of complex, real-world data into clean, consistent, and reliable datasets for machine learning and product teams. You'll architect high-throughput pipelines, establish data quality and reproducibility standards, and build the infrastructure and tooling needed to make data easy to discover, process, label, and use.
What You'll Do
- Architect and build high-throughput data pipelines from scratch
- Develop pipelines for processing, aligning, transforming, and calibrating complex datasets
- Build backfill and reprocessing frameworks for historical data
- Establish dataset lineage, versioning, and reproducibility
- Build APIs and tooling for dataset discovery and access
- Develop data-quality metrics, monitoring, and alerting
- Optimize storage, compression, partitioning, and data access patterns
- Build internal tools and workflows for ML and data-labeling teams
- Partner closely with ML engineers to understand and support their data requirements
What We're Looking For
- 5–8 years of software or data engineering experience
- Proven ability to architect a data platform or high-throughput pipeline from a blank page
- Strong Python and data tooling experience, such as PyArrow, Polars, Pandas, NumPy, or SciPy
- Experience with AWS or another major cloud platform and deploying production data pipelines
- Experience with a services language such as Go, Rust, or TypeScript
- Strong understanding of data lineage, versioning, reproducibility, and data quality
- Experience building backfill and reprocessing systems
- High ownership and ability to operate independently in a startup environment
- U.S. citizenship required
Strong Plus
- Founding or early data-platform engineering experience
- Experience with sensor, audio, video, telemetry, or geospatial data
- Airflow, Prefect, MLflow, W&B, or similar tooling
- Experience building labeling tools or ML data workflows
- Strong full-stack/generalist capabilities
- Experience treating datasets as a product, with strong documentation and discoverability