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Remote Defense Jobs in Minnesota (NOW HIRING)

Motorola

... defense, law enforcement and public safety agencies around the world, and in the toughest ... This position is eligible for 100% remote work depending on location. The following is a list of at ...

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Remote Defense information

See Minnesota salary details

$24.3K

$90.9K

$173.9K

How much do remote defense jobs pay per year?

As of Aug 8, 2026, the average yearly pay for remote defense in Minnesota is $90,887.00, according to ZipRecruiter salary data. Most workers in this role earn between $53,439.00 and $119,023.00 per year, depending on experience, location, and employer.

What is a remote defense?

A Remote Defense job involves protecting digital assets, networks, and systems from cyber threats while working from a remote location. Professionals in this role monitor security alerts, analyze threats, and implement defense mechanisms to safeguard sensitive data. They may work with firewalls, intrusion detection systems, and threat intelligence tools to prevent cyberattacks. Remote Defense specialists often collaborate with IT and cybersecurity teams to improve security protocols and respond to incidents efficiently. This role requires strong analytical skills, knowledge of cybersecurity best practices, and familiarity with security technologies.

What are the key skills and qualifications needed to thrive in the remote defense position, and why are they important?

To excel in a Remote Defense position, you’ll need expertise in cybersecurity or security operations, a thorough understanding of risk assessment, and typically a bachelor’s degree in computer science or a related field. Familiarity with network security tools, incident response platforms, and certifications like CISSP or CompTIA Security+ are highly valued. Strong analytical thinking, problem-solving, and clear written communication are important soft skills for remote collaboration and precise incident reporting. These qualifications and skills are essential for effectively identifying, mitigating, and communicating security threats in a distributed environment.

What are typical day-to-day responsibilities for someone working in remote defense?

In a Remote Defense role, your typical day often involves monitoring security alerts, analyzing suspicious activity, and coordinating incident response across remote networks and platforms. You’ll collaborate with IT teams and other security professionals to investigate potential threats, develop mitigation strategies, and document findings. The role may also require participation in regular cybersecurity drills, updating security protocols, and recommending enhancements to existing defenses. Working remotely, you’ll leverage virtual collaboration tools to stay connected with your team and ensure swift communication during security events. This dynamic environment requires both proactive and reactive approaches to safeguard organizational assets from evolving cyber threats.

What are the most commonly searched types of Defense jobs in Minnesota? The most popular types of Defense jobs in Minnesota are:
What are popular job titles related to Remote Defense jobs in Minnesota? For Remote Defense jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Remote Defense jobs in Minnesota look for? The top searched job categories for Remote Defense jobs in Minnesota are:
What cities in Minnesota are hiring for Remote Defense jobs? Cities in Minnesota with the most Remote Defense job openings:
Remote Defense jobs near you
Infographic showing various Remote Defense job openings in Minnesota as of August 2026, with employment types broken down into 89% Full Time, 7% Part Time, 3% Contract, and 1% Nights. Highlights an 91% Physical, 3% Hybrid, and 6% Remote job distribution, with an average salary of $90,887 per year, or $43.7 per hour.

Senior AI Engineer - Agentic Systems & Data Pipelines

Collaboration.Ai

Minneapolis, MN • On-site, Remote

$110K - $150K/yr

Full-time

Posted 10 days ago

Job description

Who We Are
Collaboration.Ai is a mission-focused, AI-powered software and services company based in Minnesota, with employees, partners, and customers around the world. We unite people, technology, and purpose to accelerate breakthroughs that transform industries, empower communities, and create a more sustainable future. We collaborate with organizations across the defense ecosystem, helping them navigate complex challenges and drive transformative change.
Our Products
NetworkOS - NetworkOS is an AI-powered platform that aligns people, purpose, ideas, and expertise in real-time, generating actionable insights to propel movements forward.
CrowdVector - CrowdVector is an integrated solution marketplace and innovation management platform that rapidly uncovers new ideas and advances breakthroughs to fuel movements.
To learn more about us, visit collaboration.ai.
About the Role
You'll build the agentic systems and data pipelines behind NetworkOS's AI capabilities: production agent workflows built on industry-leading agent SDKs and harnesses, MCP servers, and Agent Skills standards; the eval and observability layer that keeps LLM quality measurable; and the ingestion pipelines that turn messy, diverse data sources into queryable knowledge.
This is an execution seat, not an ivory tower. You'll commit code every week, ship agents as product capability rather than demos, and help shape a roadmap that's heading deep into graph + agents territory - for customers in defense, healthcare, and regulated enterprise.
Agents in production. Pipelines that hold. Evals that keep everyone honest.
This opportunity is remote with a preference for candidates in the Twin Cities area (Minneapolis, Saint Paul); however all candidates are encouraged to apply!
What You'll Do
  • Ship production agent systems - design, build, and operate agentic workflows (agent SDKs, MCP servers, Agent Skills standards) powering AI-driven matching, analysis, and data intelligence
  • Operationalize LLM quality - build the eval and observability layer with Langfuse, golden datasets, LLM-as-judge patterns, and FinOps-style tracking so every workflow has measurable quality, cost, and latency
  • Engineer data pipelines - robust ingestion of documents, structured data, and external sources into searchable knowledge bases with quality validation, deduplication, and incremental updates
  • Own retrieval quality - hybrid search combining vector, keyword, and metadata retrieval, continuously improved through reranking, query expansion, and contextual compression
  • Accelerate with AI - build custom MCP tools and Agent Skills that make the whole engineering team measurably faster
  • Execute alongside the team - pair with full-stack engineers on AI integration points, contribute to incident response for AI services, and keep your hands in the code
Our Tech Stack
  • Languages: Python (primary); Kotlin (core platform language at CAI); TypeScript/Node.js and other modern languages (secondary)
  • AI/ML: FastAPI, Pydantic; multi-provider LLM SDKs (Anthropic, OpenAI, and others)
  • Agentic Tooling: Claude Code/Codex/etc.; industry-leading agent SDKs and harnesses; MCP servers; Agent Skills standards
  • LLM Operations: Langfuse + evals (golden datasets, LLM-as-judge); in-house FinOps tracking (token usage, latency, cost); multi-provider orchestration including AWS Bedrock
  • Search & Retrieval: Vector databases, OpenSearch, embedding models
  • Data: PostgreSQL, Amazon S3; streaming pipelines (Kafka/Kinesis) where needed
  • Infrastructure: Docker, Kubernetes (AWS EKS); DataDog + OpenTelemetry observability
What We're Looking For
Must Haves
  • 7+ years of professional software engineering experience, with 3+ years focused on AI/ML or data engineering
  • Production agentic/LLM application experience - built and operated systems around LLM APIs (Anthropic, OpenAI) serving real users: agents, tool-use, or orchestrated LLM workflows
  • Data engineering background - robust, scalable pipelines for AI/ML workloads
  • LLM operations experience - evals and observability for production LLM systems (quality, cost, latency)
  • Production retrieval experience - vector databases and/or search engines (OpenSearch, Elasticsearch)
  • Modern Python stack proficiency - FastAPI, Pydantic, async/await, modern dependency management
  • AI-native workflows - demonstrated ability to leverage Claude Code/Codex or similar agentic coding tools to accelerate development
  • Experience with Docker, Kubernetes, and AWS
  • US citizenship required (DoD contracting - IL4/IL5 environments - and FedRAMP compliance)
Nice-to-Haves
  • Deep agentic ecosystem experience - Agent Skills standards, custom MCP servers, agent SDKs across major vendors
  • Advanced RAG expertise - GraphRAG, agentic RAG, contextual retrieval, reranking strategies
  • Graph data experience - knowledge graphs, graph databases, or graph-based retrieval
  • Model selection & rightsizing - matching models to domain-specific use cases across quality, cost, and latency tradeoffs
  • Streaming data experience (Kafka, Kinesis) for real-time knowledge base updates
  • Research background, open-source contributions, or an advanced degree in ML/IR/NLP
Why Join Collaboration AI?
Real AI engineering, not a wrapper shop. Production agents, hybrid retrieval, continuous evals, and a roadmap heading into graph + agents - with the autonomy to shape how it's built.
AI-native by default. We build with AI, not just for AI. Agentic coding tools (Claude Code/Codex/etc.), agent SDKs and harnesses, MCP servers, and Agent Skills standards are how we work daily - you'll both use and build them.
Work that matters. Defense, healthcare, and regulated industries - SOC 2 and NIST compliance, FedRAMP readiness, and customers whose missions demand AI they can trust.
Small, senior team. Early-stage impact with your work visible from week one. You'll help set the bar for how AI engineering is done here.

Apply