Senior Software Engineer, Data Engineering

Distributed Spectrum Inc.; New York (NY); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Equity

Early Series A Equity

Health, dental, and vision coverage

401(k) match up to 4%

Flexible PTO

Daily office lunches in NYC

Job summary

Distributed Spectrum Inc. is hiring a Senior Data Engineer to design a data lakehouse turning high-rate radio data into research-ready assets for ML models and real-time agents. You’ll own ingestion to access, with a focus on AI/ML workloads, and partner with researchers to self-serve data.

You will build LVL components for embedding pipelines, feature stores, and recall systems, while ensuring schema standards, reliability, and cost efficiency across petabyte-scale storage.

Qualifications

• 4–6+ years of software / data engineering experience, incl. large-scale platforms.
• Deep experience with lakehouse architectures: Iceberg, Delta Lake, Hudi, Parquet, Spark, Flink, Trino.
• Hands-on vector databases & embedding retrieval, indexing trade-offs and scaling.
• Strong AWS experience with storage, services, compute, cost optimization.
• Fluency in Python and SQL; experience with orchestrators like Airflow, Dagster, Prefect.
• Experience designing data architectures for ML/AI workloads: training data pipelines, feature stores.
• Strong data modeling skills and batch vs streaming trade-offs.

Responsibilities

• Architect and build our data lakehouse: ingestion pipelines, formats, partitioning, governance.
• Design and operate vector DB infrastructure for embedding storage and retrieval at scale.
• Build data management systems: lineage, versioning, quality monitoring, cost management.
• Create ML-focused data platform capabilities: feature pipelines, datasets, low-latency retrieval.
• Establish schema standards and tooling for self-serve research & engineering.
• Own reliability and performance of data pipelines in production with observability.
• Mentor engineers and lead technical design across the data domain.

Skills

Python

SQL

Data engineering

Cloud platforms

Vector databases

ML data pipelines

Mentoring engineers

Tools

Apache Iceberg

Delta Lake

Hudi

Spark

Flink

Trino/DuckDB

Job description

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.

The Role

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.

What you'll do

• 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.
  

What we're looking for

• 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.

Nice to have

• 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.

Who Thrives at Distributed Spectrum

• 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.

What We Offer

• Above-market salary, equity, and benefits package.

• Early Series A Equity

• Excellent health, dental, and vision coverage

• 401(k) match - up to 4% of your salary

• Flexible PTO

• Daily office lunches in NYC

ITAR Requirements

To conform to U.S. Government technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.