---
title: 'Senior Software Engineer, Data Engineering at Distributed Spectrum'
canonical: 'https://feeny.ai/job/senior-software-engineer-data-engineering-distributed-spectrum-new-york-qhnpq7ceg0d4'
type: 'job'
last_seen: '2026-10-15'
---

# Senior Software Engineer, Data Engineering at Distributed Spectrum

- **Company:** Distributed Spectrum
- **Location:** New York, NY
- **Compensation:** $160k–$200k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-06
- **Last confirmed live:** 2026-10-15
- **Apply:** https://jobs.ashbyhq.com/distributed-spectrum/6c93c6b1-5caa-42b8-a5c8-653c0c34e745

## 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](https://www.pmddtc.state.gov/?id=ddtc_kb_article_page&sys_id=24d528fddbfc930044f9ff621f961987).

## About Distributed Spectrum

## Company Overview
- **One-liner**: Distributed Spectrum builds AI-enabled software and sensors that allow anyone to detect, classify, and understand critical radio signals without requiring expensive hardware or specialized expertise.
- **Entity Type**: Private (Series A)
- **Headquarters**: New York, New York, United States
- **Founded**: 2020
- **Founders**: Alex Wulff, Ben Harpe, Isaac Struhl

## Core Business
- **Primary industry**: Defense technology / Electronic warfare / Signal intelligence
- **Target customers**: Government, defense, and commercial organizations that need real-time spectrum awareness and threat detection
- **Mission or purpose**: “Build AI and sensors to let anyone understand critical radio signals in any mission” – [distributedspectrum.com](https://www.distributedspectrum.com/about)

## Products & Services
- **Detection Mesh**: A network of hot-swappable, off-the-shelf commercial components that creates a vast, AI-powered radio frequency detection system. Provides real-time alerts with unlimited downstream integrations.
- **AI-Enabled Workflows**: Machine learning models that automatically identify and classify signals, enabling operators to act on threats without manual analysis.
- **Deploy Everywhere Platform**: Software that bridges the gap between $100 hardware and legacy $1M+ systems, allowing rapid deployment into existing infrastructure.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Total funding of $25.2M as of April 2025. Series A of $25M, led by Conviction, Shield Capital, and Nat Friedman, with participation from Felicis and XFund. – [linkedin.com](https://www.linkedin.com/company/distributed-spectrum)
- **Notable Investors/Partners**: Conviction, Shield Capital, Nat Friedman, Felicis, XFund, National Science Foundation (grant). Board Director Sarah Guo (Conviction).
- **Growth Signals**: 100% YoY employee growth (32 people), named to American Dynamism 50 in 2025, oversubscribed Series A, strong hiring momentum across engineering and mission operations roles.

## Competitive Advantages
- **Software-first approach**: Shifts focus from expensive proprietary hardware to modular, flexible software running on commercial off-the-shelf (COTS) components.
- **Hard technical moat**: Combines machine learning, embedded systems, and signal processing – a net-new problem space with few competitors.
- **Talent density**: Founders bring deep expertise from Google, Raytheon, Lockheed Martin, Microsoft, Bridgewater; entire team is technical and mission-driven.
- **Speed of deployment**: Can integrate with existing $1M+ systems or run entirely on low-cost hardware, making spectrum monitoring accessible to a wider set of missions.

## Strategic Focus
- **Product expansion**: Scaling the detection mesh and AI workflows to cover more signal types and operational environments (e.g., tactical UAVs, special projects).
- **Hiring for growth**: Actively recruiting for roles in embedded engineering, RF systems, mission operations, and program management to support government and commercial contracts.
- **Go-to-market**: Building out sales and business development teams to penetrate defense and critical infrastructure verticals.

## Why Work Here
- **Culture**: "High growth and high ownership" – trust by default, side-by-side problem solving in the office. Small, elite team where everyone contributes across ML, signal processing, and user design.
- **Work policy**: In-office (New York City). Employees work from physical offices at 147 West 25th Street, 4th Floor, NYC. – [builtin.com](https://builtin.com/company/distributed-spectrum)
- **Engineering culture**: Work on a net-new, technically hard problem with immediate real-world impact in national security. Opportunity to bridge research and production systems.
- **Perks**: Not explicitly listed, but the team is venture-backed and operates with startup intensity – likely standard benefits for a Series A defense tech company.

## Sources
1. [distributedspectrum.com – Home](https://www.distributedspectrum.com/)
2. [distributedspectrum.com – About](https://www.distributedspectrum.com/about)
3. [LinkedIn – Distributed Spectrum](https://www.linkedin.com/company/distributed-spectrum)
4. [Built In – Distributed Spectrum](https://builtin.com/company/distributed-spectrum)
5. [Ashby Careers – Distributed Spectrum](https://jobs.ashbyhq.com/distributed-spectrum)

## Other roles at Distributed Spectrum

- [Senior Software Engineer, Platform Engineering](https://feeny.ai/job/senior-software-engineer-platform-engineering-distributed-spectrum-new-york-jtz17ze2g845) — New York, NY
- [Senior Software Engineer, UI Development](https://feeny.ai/job/senior-software-engineer-ui-development-distributed-spectrum-new-york-qdb8d5hgyk88) — New York, NY
- [Senior Software Engineer, Backend Systems](https://feeny.ai/job/senior-software-engineer-backend-systems-distributed-spectrum-new-york-rmjddtfd8x93) — New York, NY
- [Engineering Manager (Embedded)](https://feeny.ai/job/engineering-manager-embedded-distributed-spectrum-new-york-s4y6b2t6dj67) — New York, NY
- [Product Operations](https://feeny.ai/job/product-operations-distributed-spectrum-new-york-mzhvvpgyrhxk) — New York, NY
- [Operations Associate, Growth](https://feeny.ai/job/operations-associate-growth-distributed-spectrum-new-york-twsse9pdfsyg) — New York, NY
- [Associate Director, Business Development - Army](https://feeny.ai/job/associate-director-business-development-army-distributed-spectrum-new-york-0x6ddj970h79) — New York, NY
- [Mission Operations](https://feeny.ai/job/mission-operations-distributed-spectrum-new-york-2wdwav9ddm8g) — New York, NY
- [RF Systems Engineer](https://feeny.ai/job/rf-systems-engineer-distributed-spectrum-new-york-ft4x6edfd49q) — New York, NY
- [Mission Operations Engineer](https://feeny.ai/job/mission-operations-engineer-distributed-spectrum-new-york-v7espz5g5wc0) — New York, NY