Senior Software Engineer, Data Engineering

DS creates systems that power the next generation of radio spectrum intelligence. We collect radio data from all over the world, train neural networks to decipher it, and run them on the smallest chips we can. We’re solving a new, technically hard problem where nothing from other fields works out of the box, and along the way, we’ve built our own stack from scratch, including entirely new embedding model architectures, custom GPU kernels, and much more.

Joining DS means owning major parts of a fast-growing AI research organization, joining a collaborative, talent-dense team with decades of experience in probabilistic ML, accelerated computing, embedded systems, and signal theory, and growing your career in the areas that interest you. You’ll fit in if you want to come to work for the problem itself and don’t want to choose between technical rigor, business value, and real-world impact.

We work with high ownership and trust.

DS collects radio data at a scale few organizations ever see — continuous, high-rate streams from sensors around the globe. We're hiring a Senior Data Engineer to design the platform that turns that firehose into an asset: a data lakehouse that serves researchers training models, production systems running inference, and agents retrieving context in real time.

You'll own the architecture from ingestion through storage, cataloging, vector search, and access, and you'll design it explicitly for AI/ML workloads, not just analytics.

• →

  Architect and build our data lakehouse: ingestion pipelines, open table formats, partitioning and compaction strategies, cataloging, and governance.

• →

  Design and operate vector database infrastructure for embedding storage, similarity search, and retrieval at scale — choosing, tuning, and evolving the right systems for our workloads.

• →

  Build large-scale data management systems: lifecycle and retention, lineage, versioning of datasets for reproducible training, quality monitoring, and cost management across petabyte-class storage.

• →

  Design data platform capabilities that directly support ML use cases — feature and embedding pipelines, training-set assembly, evaluation datasets, and low-latency retrieval for agents.

• →

  Establish schema and data-contract standards across teams, and build tooling that lets researchers and engineers self-serve.

• →

  Own reliability and performance of data pipelines in production, including observability and failure handling.

• →

  Mentor engineers and lead technical design across the data domain.
  

• 4–6+ years of software / data engineering experience, including several years designing and operating large-scale data platforms in production.

• Deep experience with lakehouse architectures and technologies — e.g., Apache Iceberg, Delta Lake, or Hudi; Parquet; Spark, Flink, Trino, DuckDB, or similar engines.

• Hands-on experience with vector databases and embedding retrieval (e.g., pgvector, Milvus, Qdrant, Weaviate, Pinecone, LanceDB, or FAISS-based systems), including indexing trade-offs and scaling.

• Strong experience with AWS or another major cloud provider — object storage, managed data services, compute, and cost optimization at scale.

• Fluency in Python and SQL; experience with orchestration tools (Airflow, Dagster, Prefect, Step Functions, etc.).

• Demonstrated experience designing data architectures specifically for ML / AI workloads: training data pipelines, feature stores, dataset versioning, or retrieval systems.

• Strong grasp of data modeling, consistency, and the trade-offs between batch and streaming.

• Experience with streaming ingestion at high volume (Kafka, Kinesis, Pulsar).

• Experience with time-series, geospatial, or signal / sensor data.

• Familiarity with data governance and security requirements in regulated environments.

• Experience with Rust, Go, or C++ for performance-critical data paths.

• Fast learners over specific backgrounds – We care more about how quickly you can pick up new skills than where you’ve worked before.

• Intellectual honesty – The right answer matters more than being right. You challenge assumptions, test ideas, and pivot when needed.

• Adaptability – We’re organized, but sometimes things change quickly. You find a way to make it work and balance short-term deliverables with long-term goals.

• Ownership of outcomes – You optimize your own time, focus on what matters to deliver quickly, and cut out inefficiencies.

• Not building in a vacuum – You stay connected to the rest of our teams and our customers to make sure all the pieces fit together.