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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. Originally posted on Himalayas