The Opportunity:
Satlantis US is building its core computer vision and machine learning function from the ground up, and we are looking for our Founding Computer Vision & ML Engineer / Tech Lead.
In this role, you will own the technical direction for imagery understanding and applied machine learning across our cutting-edge Earth-observation products-and you will build and lead the team that delivers them.
You will report directly to a CTO with deep machine learning expertise, giving you a genuine technical sparring partner rather than a corporate management layer. This is a true 50/50 hands-on leadership role: you will spend half your time prototyping complex vision problems, reviewing architectures, and writing code, and the other half setting the roadmap, scaling your team, and collaborating with our mission teams and international engineering counterparts in Spain.
If you want your judgment about vision systems to fundamentally shape a company's product line rather than just a single workstream, this is your platform.
What You'll Own: - The CV/ML Roadmap: Define our 12-18 month strategy for Earth-observation imagery understanding (segmentation, detection, change detection, semantic retrieval, and anomaly detection).
- Team Building & Culture: Hire, mentor, and grow a high-performing engineering team, establishing rigorous standards for evaluation, reproducibility, and peer review.
- Production & Scale: Partner with software and platform engineering to ship models into production, maximizing resources like the University of Florida's HiPerGator AI supercomputer.
- Global Collaboration: Partner seamlessly with image processing and mission peers at our headquarters in Spain to align on shared standards, tooling, and datasets.
What We're Looking For (required): - Experience: 5+ years in computer vision, machine learning, or applied AI, with a proven track record of delivering vision models into operational use or production.
- Leadership: 2+ years leading engineers as a manager or technical lead (including hiring, mentorship, and team accountability).
- Technical Depth: Deep command of computer vision fundamentals (image representations, geometric reasoning, dense prediction, detection, segmentation).
- Stack Fluency: Strong Python skills with hands-on depth in PyTorch and practical fluency in training/inference optimization.
- Mindset: A builder who loves writing clean, maintainable code just as much as setting architectural vision.
Location & Work Model: This is a full-time, on-site role in Gainesville, Florida. Leading this team means being in the room with the engineers and product colleagues you work alongside; we are not able to offer a remote arrangement for this position.
Work Authorization: MUST BE A U.S. CITIZEN - The selected candidate must be eligible to obtain and maintain a Secret security clearance. This position does not provide employment visa sponsorship; candidates must possess permanent, unrestricted legal authorization to work in the U.S.
Compensation and Benefits:
Salary is competitive and commensurate with experience, supplemented by a performance-based bonus. Our comprehensive benefits package includes medical, dental, and vision coverage, along with paid time off.
How to Apply
To apply for the
Founding Computer Vision and ML Engineer / Tech Lead - Earth Observation position, please submit your materials using the two separate upload fields below.
We want to understand not just what you build, but how you navigate the complexities of real-world deployment. In addition to your professional history, please share your technical reflections with us:
- Resume / CV
- Upload your latest resume highlighting your experience building, deploying, and scaling machine learning systems.
- Production Case Study Note
- Provide a short note focusing on a computer vision system you personally led into production. Please candidly address:
- What you decided: Your core engineering choices, architecture, or model selections.
- What you got wrong: The unexpected real-world edge cases, scaling bottlenecks, or failures the system encountered.
- What you would do differently: The technical adjustments or alternative approaches you would take today based on what you learned.
