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About This Role
Who We Are
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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
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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
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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
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- 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
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- 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
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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.
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Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Collaboration.Ai, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Collaboration.Ai AI Hiring
Collaboration.Ai has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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