---
title: 'Machine Learning Engineer at Distributed Spectrum'
canonical: 'https://feeny.ai/job/machine-learning-engineer-distributed-spectrum-new-york-r5bca8s2qp52'
type: 'job'
last_seen: '2026-09-13'
---

# Machine Learning Engineer at Distributed Spectrum

- **Company:** Distributed Spectrum
- **Location:** New York, NY
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-03-18
- **Last confirmed live:** 2026-09-13
- **Apply:** https://jobs.gem.com/distributed-spectrum/am9icG9zdDrNwd-aMeOa9NOTWPhGvHjf

## Job description

We’re hiring a Machine Learning Engineer to take ownership of developing and optimizing creative models for understanding the radio spectrum. You’ll be responsible for translating high-level problem statements into working machine learning solutions—owning the entire process from research to implementation. Your work will have an immediate impact, driving critical improvements to our product and delivering real results in the field.

This role requires someone with incredibly strong fundamentals in math, statistics, and programming with the creativity to find the best solutions in a novel domain. You’ll work closely with embedded engineers, leadership, and our customers to ensure that our models not only work in theory but perform in the real world.

What you’ll do:

- Given a well-defined (but difficult) problem, an objective function, and guidance on possible research areas, you’ll identify, develop, and implement the best ML solution.
- Own the entire machine learning pipeline—research, data management, model development, and testing.
- Build proofs-of-concept, validate them with customers in the field, and rapidly iterate based on feedback.
- Partner with collaborators on the MLE team to continuously develop and refine ML practices and infrastructure.
- Continuously optimize and refine models to improve performance, accuracy, and efficiency.
- Work closely with the embedded systems team to integrate machine learning models into our products.
- React quickly to changing requirements, identifying bottlenecks and adjusting approaches as needed.
- Grow fast with real opportunities – We’ll keep expanding your scope and giving you bigger challenges to help you reach your goals. If you don’t know exactly what role you want to grow into, you’ll have the freedom to take on different responsibilities and find the right path. Who we’re looking for (every role):
- 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. Who we’re looking for (this role):
- You’ll need to be able to operate in an environment with uncertainty and evolving mission requirements, balancing research with practical implementation.
- Strong experience in machine learning and statistical analysis, with a proven track record of applying research to real-world problems.
- Deep proficiency in Python, PyTorch, and related frameworks, plus familiarity with Linux.
- Ability to work across disciplines—collaborating with embedded engineers, leadership, and customers to refine and deploy solutions.
- Strong communication skills, ensuring complex technical concepts are clearly understood by all stakeholders. What we offer:
- Above-market salary, equity, and benefits package. In accordance with NY regulations, the salary range for this position is $100,000-$300,000 to cover a broad range of candidate experience.
- Excellent health, dental, and vision coverage
- 401(k) match - 100% up to 4% of salary
- Unlimited PTO
- Daily office lunches in NYC

## About Distributed Spectrum

## Company Overview
- **One-liner**: Distributed Spectrum builds AI-powered software and sensors that use commodity hardware to detect, identify, and track radio signals for defense and security missions.
- **Entity Type**: Private (Venture-backed; Series A)
- **Headquarters**: New York, New York, United States
- **Founded**: 2020
- **Founders**: Alex Wulff (CEO), Ben Harpe (COO), Isaac Struhl (CTO)

## Core Business
- Primary industry/industries: Defense technology, signal processing, electronic warfare, artificial intelligence
- Target customers: U.S. Department of Defense (DoD), Intelligence Community (IC), and other government/security stakeholders (B2G)
- Mission or purpose statement: To let anyone understand critical radio signals in any mission, bridging the gap between $100 off-the-shelf hardware and existing $1M+ legacy systems.

## Products & Services
- **Distributed Spectrum Platform**: An AI-enabled software platform that processes radio frequency data locally on edge devices. It identifies and tracks threats (carried, driven, or left behind) and provides real-time alerts and autonomous threat mapping without requiring expensive hardware or trained experts. Type: SaaS + Hardware (sensors).

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total Funding of **$25.2M** (Series A of $25M led by Shield Capital and Conviction in April 2025; prior funding includes a $231,904 NSF Grant in 2021)
- **Notable Investors/Partners**: Shield Capital, Conviction (Sarah Guo), Nat Friedman (NFDG), National Science Foundation
- **Growth Signals**: Secured $7M in contracts across DoD and IC within 60 days prior to their Series A. Headcount grew 111% YoY (from ~12 to 32 employees as of mid-2025). The company has filed 6 patents, with one granted in August 2025 for "Radio-frequency signal processing systems and methods."

## Competitive Advantages
- **Software-Defined Disruption**: Shifts focus from expensive, bespoke hardware to modular, flexible software that works with commodity hardware, disrupting entrenched players with $1M+ systems.
- **Edge AI & Autonomy**: Processes data locally on the sensor to map threats autonomously, reducing latency and the need for human experts in the field.
- **Deep Technical Moat**: Combines expertise in machine learning, signal processing, and embedded systems—a "net-new, technically hard problem" with no existing blueprint.
- **Founding Team**: Founders have deep domain experience at Raytheon, Lockheed Martin, Microsoft, Google, and Bridgewater.

## Strategic Focus
- Scaling the engineering and AI/ML teams to meet oversubscribed demand from DoD and IC.
- Developing "RF Foundation Models" to advance machine learning for radio frequency analysis.
- Expanding from a team of 7 engineers to a larger, multi-disciplinary organization while maintaining high ownership and in-person collaboration.

## Why Work Here
- **Impact**: Solve a net-new, technically hard problem at the intersection of machine learning, embedded systems, and national security. "Too oversubscribed with demand to work on anything that's not mission critical."
- **Culture**: High growth, high ownership, and "trust by default." The team values scientific and mathematical fundamentals over cargo-culting solutions from other fields.
- **Work Environment**: **In-person, 5 days per week** in New York City (99 Madison Ave, 4th Floor). The company believes in solving problems side-by-side with talented thought partners.
- **Team**: Currently ~32 people, growing fast. Backed by world-class investors including Sarah Guo (Conviction) and Nat Friedman.
- **Open Roles**: Engineering (Embedded, RF, Systems Integration), AI/ML (RF Foundation Models), Growth (Mission Operations), and Operations.

## Sources
1. [distributedspectrum.com](https://www.distributedspectrum.com/)
2. [distributedspectrum.com/about](https://www.distributedspectrum.com/about)
3. [linkedin.com/company/distributed-spectrum](https://www.linkedin.com/company/distributed-spectrum)
4. [cbinsights.com/company/distributed-spectrum](https://www.cbinsights.com/company/distributed-spectrum)
5. [jobs.ashbyhq.com/distributed-spectrum](https://jobs.ashbyhq.com/distributed-spectrum)

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