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About This Role
Stratus, deriving from the Latin term meaning 'layer', offers an advanced set of MEP specific solutions that seamlessly layer across a contractor's entire workflow from design to fabrication to installation. Our team of seasoned industry experts, skilled technology leaders, innovators, and entrepreneurs understands that fabrication does not occur in isolation, and increasingly, it may not happen within your own fabrication shop. Through close relationships with our customers—who include some of the most innovative and largest MEP contractors—we have developed a suite of Stratus tools to digitize, automate, and optimize piping, plumbing, sheet metal, and electrical contracting. Stratus provides the software layer an MEP Contractor needs to optimize profits with true "Data Driven Contracting."
GENERAL DESCRIPTION:
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The Senior Software Engineer with AI plus Revit builds the customer\-facing AI layer for Stratus — the production agent systems, tool integrations, and evaluation infrastructure that let our products reason over MEP fabrication data and act on a contractor's behalf. This is a hands\-on senior engineering role: you will design and ship multi\-agent workflows, build the tool and context layer that connects agents to Stratus data, and own the guardrails, evals, and observability that make those systems safe and trustworthy in front of customers.
This is a dual\-strength role. Your primary craft is building customer\-facing AI applications. Your second required competency is the Autodesk Revit API: much of the design and fabrication data our agents reason over originates in Revit, and the workflows we automate live at the boundary between our customers' Revit environment and the Stratus platform. You will build and integrate directly against that boundary, not merely consume data downstream of it.
Reporting to the Director of Engineering, you will work as a senior individual contributor on a small, fast\-moving team — owning meaningful pieces of the system end to end and collaborating closely with product and customer\-facing teams.
We are an AI\-forward engineering team. We expect every engineer to use AI\-assisted development tooling (Claude Code, Cursor, Copilot, and the like) as a first\-class part of the dev loop — and to exercise sharp judgment about when AI output is shippable, when it needs rework, and when it should be thrown away.
KEY RESPONSIBILITIES:
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### AI engineering (primary)
- Design and build customer\-facing agentic workflows: multi\-agent orchestration (e.g., LangGraph, CrewAI, AutoGen), tool calling, structured outputs, multi\-step planning, and human\-in\-the\-loop checkpoints, to automate complex MEP engineering tasks.
- Establish evaluation, guardrails, and failure\-mode analysis for agent systems — including offline eval suites in CI and live production sampling — to ensure they are safe, reliable, and grounded.
- Build the tool and context layer connecting agents to Stratus data via internal and customer\-facing APIs (e.g., MCP), including context\-window management, permissioning, and cost control.
- Set up observability and tracing for agent behavior; diagnose cost, latency, and hallucination issues in production.
- Integrate Stratus's published design and fabrication data into agent workflows.
### Revit integration (required, secondary)
- Build and maintain the Revit\-side integrations that feed the AI layer — the add\-ins and publishing paths that move design and fabrication data out of our customers' Autodesk Revit environment and into Stratus.
- Write production C\#/.NET code against the Revit API: custom commands, external events, document and transaction management, and integration with Autodesk model data.
- Own the fidelity, mapping, validation, and error handling that keep Revit data exports trustworthy enough for agents to reason over.
- Solve the hard problems specific to Revit add\-in development — version compatibility, performance inside large models, the API threading model, and graceful degradation when the host environment misbehaves.
### Collaboration \& delivery
- Use AI\-assisted development tooling (Claude Code, Cursor, Copilot, etc.) as a first\-class part of the dev loop — writing tests for AI\-generated changes and exercising clear judgment about when AI output is ready to ship.
- Collaborate with product managers, designers, and customer\-facing teams to scope, design, and ship — grounding technical decisions in real design and fabrication workflows.
- Perform requirements analysis with stakeholders, ensuring solutions meet immediate product goals and longer\-term objectives.
- Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs.
- Share knowledge and raise the engineering bar through code review and pragmatic best practices.
QUALIFICATIONS:
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### Required:
- 6\+ years of software engineering experience, with a proven track record of shipping and operating production\-grade systems — not just prototypes or notebooks.
- Primary — AI engineering: Hands\-on experience building and operating customer\-facing agentic systems in production — orchestration frameworks (LangGraph, CrewAI, or AutoGen), tool calling, structured outputs, and eval frameworks. Experience with evals, guardrails, and observability for LLM or agent systems.
- Secondary — Revit API: Hands\-on production experience building Revit add\-ins and working within the realities of the Revit API — the document and transaction lifecycle, external events, the threading model, version compatibility, and performance inside large models.
- Strong proficiency in C\#/.NET, with demonstrated production ownership of real features.
- Experience with MCP or similar tool\-integration protocols.
- Strong computer science fundamentals (data structures, algorithms, system design) and solid API/backend engineering depth.
- Hands\-on use of AI\-assisted development tooling (Claude Code, Cursor, Copilot, or equivalent) as a first\-class part of your daily workflow, with clear judgment about when AI output ships, needs rework, or should be thrown away.
- Excellent communication skills — able to explain complex AI systems clearly to teammates, product partners, and customers.
- Comfort working in newly forming, ambiguous areas where learning and adaptability are key.
- Degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
### Nice to Have:
- Familiarity working with data derived from CAD/BIM or other 2D/3D model sets.
- Working familiarity with the broader Autodesk application family (e.g., AutoCAD, Fabrication, BIM 360 / ACC, Navisworks) and the Autodesk Platform Services ecosystem (formerly Forge) — Data Management, Model Derivative, Design Automation, or related APIs.
- Desktop application development experience — including installers (MSIs) and managing packaging, deployment, and updates across customer environments.
- Familiarity with machine learning concepts and how data is represented for training.
- Domain knowledge of MEP, BIM, or construction fabrication workflows, or an architectural/AEC engineering background.
- Full\-stack development experience beyond the desktop application.
- Background in customer\-facing or professional\-services roles.
- Drive to continually learn new technologies and seek new ways to solve hard problems.
- Bias toward putting your ideas out there and failing fast.
BENEFITS:
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- Comprehensive and competitive health benefits plan
- Matching 401k contributions
- 20 days annual PTO
- Primarily remote work with occasional annual team onsites.
This is a remote role, but candidates must be based in the U.S.
E\-VERIFY STATEMENT
*Stratus participates in E\-Verify. After you join the team, we'll verify your eligibility to work in the U.S. by submitting information from your Form I\-9 to the Social Security Administration and, if needed, the Department of Homeland Security. This process happens post\-hire only — we never use E\-Verify to pre\-screen applicants.*
*E\-Verify Notice*
*Right to Work Notice*
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Stratus, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Stratus AI Hiring
Stratus has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Frequently Asked Questions
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