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Llm Remote Jobs in Minnesota (NOW HIRING)

UnitedHealth Group

Lead Data Scientist - Remote

Minnetonka, MN · On-site +1

$112K - $193K/yr

  • Retirement

Contribute to skill development for engineers and interns in AI/LLM techniques * Comply with company policies, procedures, and directives You'll be rewarded and recognized for your performance in an ...

Kobie Marketing

AI Engineer

Minneapolis, MN

As a remote-first organization headquartered in St. Petersburg, Florida, Kobie values meaningful in ... Build evaluation harnesses (golden datasets, LLM-as-judge, regression suites) using AgentCore ...

A.C. Coy

Cloud Security Engineer/Architect (Remote)

Eagan, MN · Remote

$66.75 - $88.75/hr

This remote contract-to-hire position can be originated in Falls Church, VA, Morrisville, NC or ... securing LLM-integrated applications against emerging attack vectors. * Automated Guardrails ...

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Llm Remote information

What is an llm remote job?

An LLM Remote job typically refers to a position that involves working with large language models (LLMs) such as OpenAI's GPT, but done remotely rather than in a traditional office setting. These roles can include positions like machine learning engineer, data scientist, prompt engineer, or AI researcher, all focused on developing, fine-tuning, or applying LLMs. Working remotely allows professionals to contribute to AI projects from anywhere, often collaborating with distributed teams and leveraging cloud-based tools. This flexibility is ideal for those who want to work in the AI field without relocating to a tech hub.

What is the difference between Llm Remote vs Legal Assistant?

AspectLlm RemoteLegal Assistant
Required CredentialsLaw degree (JD or equivalent), bar admission (preferred)High school diploma or associate degree, paralegal certification often preferred
Work EnvironmentRemote, flexible hours, legal firms or corporate legal departmentsOffice-based or hybrid, law firms, corporate legal departments
Industry UsageLegal research, document review, legal analysisLegal support, document preparation, client communication
Search & Comparison IntentUnderstanding remote legal roles, legal research jobsLegal support roles, paralegal or legal assistant positions

While both roles support legal operations, Llm Remote typically involves legal research and analysis requiring a law degree, often performed remotely. Legal Assistants focus on administrative and support tasks, usually in-office or hybrid, with less emphasis on legal research. The choice depends on your credentials and preferred work environment.

What are some common challenges faced by remote large language model (LLM) engineers, and how can they overcome them?

Remote LLM engineers often face challenges such as collaborating effectively across time zones, maintaining clear communication with distributed teams, and staying updated on rapidly evolving AI research. To overcome these obstacles, it's important to leverage collaboration tools (like Slack, GitHub, and video conferencing), establish regular check-ins, and participate in virtual knowledge-sharing sessions. Additionally, proactively seeking feedback and engaging with global AI communities can help remote LLM engineers stay aligned with team goals and industry trends.

What are the key skills and qualifications needed to thrive as an llm remote engineer, and why are they important?

To excel as an LLM Remote Engineer, a solid background in machine learning, natural language processing, and proficiency with programming languages like Python is essential, often supported by a degree in computer science or a related field. Experience with frameworks such as PyTorch or TensorFlow, familiarity with large language models (LLMs), and relevant cloud platforms (like AWS or Azure) are typically required, along with certifications in AI or ML being advantageous. Strong problem-solving, communication, and self-motivation are crucial soft skills for collaborating effectively across remote teams and driving innovation. These competencies ensure successful model development, deployment, and maintenance in a distributed work environment.

What are the most commonly searched types of Llm jobs in Minnesota?

The most popular types of Llm jobs in Minnesota are:

What are popular job titles related to Llm Remote jobs in Minnesota?

For Llm Remote jobs in Minnesota, the most frequently searched job titles are:

What job categories do people searching Llm Remote jobs in Minnesota look for?

The top searched job categories for Llm Remote jobs in Minnesota are:

Llm Remote jobs near you
Infographic showing various Llm Remote job openings in Minnesota as of August 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 79% Physical, 4% Hybrid, and 17% Remote job distribution.

Senior AI Engineer - Agentic Systems & Data Pipelines

Collaboration.Ai

Minneapolis, MN • On-site, Remote

$110K - $150K/yr

Full-time

Posted 17 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