IT​/Tech, AI Engineer, Data Engineer

International Falls, MN, USA Posted on Jan 26, 2026

Passionate about building a future powered by science? Check out the opportunities in our portfolio companies.

Startups change a lot
, it’s part of what makes them exciting. At Transfyr, we’re not hiring for static roles or fixed titles. We’re looking for builders, thinkers, and doers who can help us tackle a range of “jobs to be done” that will arise over time and grow with us professionally as we grow as a company.

If you find yourself nodding along “yes, that’s me!” in more than a few categories or have true superpowers in one or two of these areas, that’s a good sign. If you’re “Hell-yes!” about our mission, adaptable across different challenges, and the kind of culture carrier others want to follow, we want to talk to you.

We’re a startup, which means everyone wears a few hats. The people who thrive here are excited to build new things, learn in public, and collaborate deeply. You might be an expert in one area, growing fast in another, and pinch‑hitting wherever needed, and that’s exactly how we like it. We are not actively recruiting for all of these roles, but want to hear from you so we can reach out if your interest and experiences align with jobs we need to get done over time.

• Build the sensor stack in the lab:
  You are an engineer who can rapidly prototype a robust sensor stack across multiple modalities (audio, visual, RFID, environmental, equipment readouts). You think carefully about how each component integrates with data pipelines and how the data will be structured for downstream use.
  • You would be excited to talk about:
    How would you capture the subtle nuances of scientific execution in a way that could train and tune a model for automated science?
• Data harmonization / segmentation:
  You excel at bringing order to messy, multimodal datasets. You can align video, audio, metadata, and instrument outputs into a coherent structure that is searchable, consistent, and ready for machine learning applications.
  • You would be excited to talk about:
    How would you approach segmentation of a multi-modal (vision, audio, tabular) time series containing information about actions, perturbations, and results?
• Computer Vision:
  You are skilled at designing computer vision systems that can extract information from images and video  understand how to build efficient search pipelines and intermediate representations that preserve meaning and enable deeper analysis.
  • You would be excited to talk about:
    How would you parse complex visual datasets, and what representations would you use to make them useful for downstream tasks?
• ML / RL Stack ( build intelligence with the data ):
  You design systems that can learn from large, multimodal datasets. You are motivated by the challenge of building pipelines that generate mechanistic insights, enable transfer learning, and support agentic reasoning about scientific execution and results.
  • You would be excited to talk about:
    How would you build ML systems that can reason about the relationship between experimental actions and scientific outcomes?
• Full Stack Engineering:
  You are a software builder who makes intelligence accessible to scientists through thoughtful interfaces and tools. You know how to translate complex models into systems that provide clear, actionable guidance at the bench.
  • You would be excited to talk about:
    What’s the right balance between speed, simplicity, and scalability in full-stack architecture? How do you know when to refactor versus when to live with a little mess? What makes an architecture “elegant” in practice, not just in theory?
• XR:
  You are thinking ahead about how immersive technologies will change the way scientific knowledge is delivered. You see opportunities to use XR for proactive, real-time guidance rather than relying on static instructions.
  • You are excited to engage us about the roadmap for XR hardware and software capabilities
• Technical Product Management (“FDE”):
  You move seamlessly between customer conversations and technical specifications. You have wet lab experience, enough engineering skill to prototype solutions, and a strong bias toward validating hypotheses by…