Drimva

STR

Lead Network Systems Researcher

$180K–$230K

Woburn, MA +2 · Full-time · Posted Oct 20, 2026

Location
Woburn, MA
Arlington, Virginia, United States
Woburn, Massachusetts, United States
Experience
PhD with 2+ years, MS with 6+ years, or BS with 8+ years of relevant experience.
Education
PhD, MS, or BS in computer science, machine learning, computer/electrical engineering, or a related technical discipline.

About the job

Leads technical execution and delivery of networking, communications, and machine-learning systems for mission-relevant applications.

What you'll do

  • Lead projects from requirements definition through development, integration, testing, demonstration, and transition.
  • Translate mission needs into requirements, architectures, plans, milestones, and acceptance criteria; manage technical tasks, dependencies, and risks.
  • Lead multidisciplinary teams and apply ML and statistical methods to networking, communications, and real-time data challenges.

What they ask for

  • PhD with 2+ years, MS with 6+ years, or BS with 8+ years of relevant experience in a related technical discipline.
  • Ability to obtain and maintain a Top Secret security clearance; U.S. citizenship is needed by the U.S. government.
  • Experience leading technical development and translating customer or mission needs into requirements, designs, plans, and integrated systems.
  • Experience applying state-of-the-art ML methods, including unsupervised and semi-supervised learning; strong networking and communications theory knowledge across the OSI model.

Nice to have

  • Active Top Secret security clearance; experience with collaborative software development and LLM-enabled coding tools such as Cursor or Codex.
  • Protocol-layer development or 5G expertise; experience supporting technical proposals, customer demonstrations, or capture activities with DoW or IC customers.

Pay

$180,000 - $230,000 USD base salary

Tools

ML, Git, JIRA, Confluence, Cursor, Codex, 5G, OSI

AI focus

Applies state-of-the-art ML, including unsupervised and semi-supervised learning, to networking, communications, real-time data, and resource-constrained edge and network-core environments.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)