Help us transform how an entire industry thinks about safety
Mojo AI is revolutionizing safety management in high-risk industries like construction and oil & gas. How are we doing this? With Safety Mojo – our groundbreaking, AI-powered app designed for frontline safety professionals. Right now, we're at an inflection point. We've proven product-market fit, we have incredible customers who love what we've built, and we're moving from startup phase into serious growth mode. The next 18 months are going to prove that we're the new standard for safety technology. And we need exceptional professionals to help us do that
The Role
We're looking for an AI Engineer who gets genuinely excited about solving real problems with AI. Not someone who just wants to chase the latest models because they're new and shiny—we want someone who asks "what will actually make workers safer?" and "what will help safety managers prevent incidents before they happen?"
This isn't demo work. You'll be shipping features that construction superintendents use every morning to figure out where they need to be. Features the frontline needs in emergencies. Features that Spanish-speaking crews use to report what they're seeing in their own words. Your work directly impacts whether someone goes home safe at the end of their day.
What You'll Actually Be Doing
Leading our workflow architecture
We need to migrate our AI workflows to N8N and build something that's actually robust and scalable, not the patchwork of tools we've been using
You'll design the workflow orchestration that connects our various AI models, data sources, and business logic in a way that makes sense
Build workflows that are observable and self-healing, because they need to scale with us as we grow quickly
Making our NLP better
Our conversational AI lets workers create safety reports just by talking—in English or Spanish. You'll optimize and fine-tune that experience
We have this feature called "Ask Mojo" that turns complex safety manuals into natural conversations. It needs to get better at making sure workers get the right answer from the right document, every time
Build context-aware understanding that knows the difference between "fall protection" on a 30-story building versus a 6-foot ladder—because that context really matters
Pushing our OCR and computer vision forward
Our Flex PTP technology extracts structured data from any safety form, whether it's a pristine PDF or a mud-stained piece of paper photographed at sunset on a jobsite. We need to make this even better
Improve accuracy on handwriting recognition, checkbox detection, form field extraction—all across wildly inconsistent formats
Build intelligence that doesn't just extract text but actually understands what it means in a safety context
Collaborating across the team
Work with product managers to turn user pain points into technical solutions that actually solve problems
Partner with other engineers to make sure your AI models integrate smoothly into the broader platform
Talk directly to customers sometimes to understand their challenges and make sure what you're building actually works for them
What Makes You A Great Fit
You've actually shipped AI to production
You know the difference between a model that's 95% accurate in testing and an 85% accurate model that users actually trust in the real world
You're comfortable across the full stack—from training models to designing APIs to orchestrating workflows
You write code that other engineers want to maintain (or at least don't hate maintaining)
You care more about impact than the technology itself
You get excited about solving a real problem imperfectly rather than building a perfect solution to the wrong problem
You measure success by what users can do, not just by model metrics
You're totally fine using "boring" technology if it's the right tool for the job
You're a self-starter who can handle ambiguity
You don't wait around for perfectly specified requirements. You talk to users, figure out the core problem, propose solutions, and execute
You can make technical decisions with incomplete information and then adjust as you learn more
Someone can give you a vague goal like "make our OCR work better on handwritten forms" and you'll turn that into a concrete plan with actual milestones
You obsess over the right details
You care about latency because a 2-second response time is the difference between a worker using your feature or avoiding it completely
You think deeply about error handling because a confusing error message on a construction site isn't just annoying—it creates real safety risk
You design for actual conditions: poor lighting, people wearing gloves, limited connectivity, multilingual users
You communicate clearly
You can explain complex technical stuff to product managers, customers, and executives in a way they understand
You write documentation that helps your teammates understand not just what your code does, but why you built it that way
You're not precious about your ideas—you care more about being effective than being right
What You Need To Have
Must haves
3+ years building and deploying AI/ML systems that actually run in production
Strong Python skills and experience with modern ML frameworks like TensorFlow or PyTorch
Real NLP experience —whether that's fine-tuning LLMs, prompt engineering, RAG, or classical NLP approaches
Hands-on work with computer vision and OCR (OpenCV, Tesseract, modern vision models, document AI services)
Workflow automation experience with N8N is our primary tool
Solid software engineering fundamentals : version control, testing, CI/CD, monitoring, observability
Nice to haves
Experience with document understanding problems—forms, invoices, receipts, scanned documents, that kind of thing
Multilingual NLP experience, especially the challenges that come with building for Spanish/English bilingual users
Background in highly regulated industries where accuracy and compliance actually matter (construction, healthcare, finance)
Experience with mobile-first or field-worker applications where connectivity is spotty and usability constraints are very real
Open source contributions or side projects that show how you think about problems
You've been at a growth-stage startup before where you had to wear multiple hats
API design and integration —you've built systems that reliably connect multiple services
Why This Role Is Different
The stakes are real. Your work doesn't just move engagement metrics—it prevents injuries. When your OCR accurately extracts a permit, when your NLP correctly understands a hazard report, when your workflow gets critical information to the right person fast enough—real people are safer because of what you built.
You'll have real ownership. We're small enough that you'll see the direct impact of your work. You'll talk to users. You'll influence the product roadmap. You'll make architectural decisions that define how we scale over the next few years.
The timing is perfect. We're at the most exciting phase of a company's life—we've proven the concept, we have product-market fit