Solutions Engineer

About Ultralytics:

At Ultralytics, we commit to relentless innovation in the AI space and seek team members who resonate with our ambition to produce the world's best YOLO AI models. If you're obsessed with AI, eager to make an impact on the world, and thrive in dynamic, high-intensity environments, we invite you to apply for a position on our team.

Join the team powering the future of vision AI
Hello, hola, 你好, こんにちは, नमस्ते, hallo, bonjour! Thanks for stopping by — we know your time is valuable, so here's what makes Ultralytics special.

⚡ Who we are
At Ultralytics, we're on a mission to simplify AI for everyone. As the creators of the world's leading Ultralytics YOLO models, we empower millions of developers, researchers, and companies worldwide to build state-of-the-art computer vision applications.

Following our $30M Series A round, we're expanding rapidly across our global hubs. This is an opportunity to join a fast-scaling, high-performance team that's redefining the future of vision AI — where ambition meets impact, and ideas become reality.

We move fast. We build boldly. We execute with purpose. And we do it together.

About the role
As a Solutions Engineer, you'll turn complex computer vision problems into production systems built on YOLO. You'll lead technical discovery, design architectures, build benchmarks and proof-of-concepts, and guide customers from their first conversation to deployed models.

You'll work across Sales, Customer Success, Product, and Engineering while partnering directly with customers in manufacturing, retail, security, robotics, and beyond. This is a hands-on role for an engineer who enjoys writing Python, training and optimizing models, deploying across cloud and edge, and owning outcomes throughout the customer lifecycle.

If you thrive on ambiguous problems, direct customer contact, and turning technical possibilities into measurable results, you'll fit right in.

What you'll do
Technical discovery and solution design

• Own technical discovery, translate customer needs into architectures, and define measurable success criteria.

• Evaluate customer data, recommend models, and design training, inference, and deployment strategies.

• Lead technical qualification and solution design alongside Account Executives and Customer Success Managers.

Prototyping and deployment

• Build Python prototypes with YOLO, OpenCV, PyTorch, and Docker.

• Create benchmarks, integrations, and production-ready training and deployment workflows.

• Deploy solutions across Linux, macOS, Windows, cloud, and edge environments.

• Diagnose performance, data quality, inference, and hardware acceleration challenges.

• Use the Ultralytics documentation and YOLO guides to deliver reliable customer outcomes.

Customer engagement and enablement

• Deliver technical demos, workshops, architecture reviews, and proof-of-concepts that move opportunities forward.

• Explain machine learning clearly to engineers, operators, executives, and other non-technical audiences.

• Build trusted relationships and manage technical stakeholders from evaluation through production.

Ecosystem contribution

• Turn customer solutions into reusable examples, deployment guides, integrations, and documentation.

• Share feedback with Product and Engineering to improve the YOLO ecosystem.

• Engage developers through the Ultralytics community and GitHub organization.

What success looks like

• 3 months: Ramped on the YOLO stack and customers, co-running discovery, and delivering your first demos and project kickoffs.

• 6 months: Owning technical discovery and POCs end to end, unblocking live deals or at-risk accounts, and earning trust from the AEs and CSMs you support.

• 9 months: Converting technical evaluations into wins or saves, shipping reusable assets, and providing actionable feedback to the wider organization.

• 12 months: Becoming the technical partner AEs and CSMs want on their biggest accounts, with POCs that convert and referenceable customer deployments.

Skills and experience
Core requirements

• Strong Python and practical experience with YOLO, OpenCV, PyTorch, and computer vision.

• Experience training, optimizing, evaluating, and deploying machine learning models.

• Comfortable with Docker, Git, REST APIs, Linux, cloud platforms, and edge workflows.

• Experience deploying across Linux, macOS, and Windows environments.

• Proven ability to deliver technical demos, workshops, architecture reviews, and POCs.

• Strong communication skills with both technical and executive audiences.

• Independent ownership, urgency, resilience, and results in a high-performance startup environment.

• Passion for AI, open source, and helping customers achieve real outcomes.

Nice to have

• Kubernetes, CUDA, TensorRT, ONNX, Jetson, or other edge accelerators.

• Experience with MLOps, model monitoring, CI/CD, or observability.

• Familiarity with AWS, Azure, GCP, serverless systems, or managed inference platforms.

• Experience integrating OpenAI or Anthropic APIs into customer tools.

• Open-source contributions or experience supporting developer communities.

• Knowledge of Ultralytics YOLO workflows and production computer vision solutions.

Why this role

• Real impact: Your solutions will power vision AI in production across systems used by millions of devices.

• Demand you don't have to manufacture: YOLO is the world's most popular computer vision model, and customers already come to us looking to do more with it.

• Build at the source: Work directly on top of the YOLO ecosystem with clear lines to Product and Engineering.

• A team that ships: Join a fast-moving, high-standard team that values results over routine.

• Customer ownership: Stay involved beyond the demo and see your technical work reach production.

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