Senior Machine Learning Engineer · AI / Computer Vision

Surveily is a start-up focused on transforming workplace health and safety standards by monitoring and improving conditions in challenging and dangerous environments. Its technology uses machine learning and real-time data to help make employees' workdays safer.

We are looking for a Senior Machine Learning Engineer to lead the development of production-grade computer vision solutions that solve real customer problems. You will drive projects across the full machine learning lifecycle, from understanding business requirements and designing technical solutions to deploying, operating, and continuously improving models in production.

We are building a team of end-to-end ML engineers rather than specialists. As a Senior Engineer, you will take ownership of complex technical challenges, influence engineering direction, mentor other engineers, and help establish best practices across the team.

Strong Python engineering skills are essential. A significant part of your work will involve designing reusable ML infrastructure, improving developer workflows, and building production-quality software that enables the entire team to move faster.

What you'll be doing

• Lead the design, development, and deployment of production-ready computer vision solutions.
• Own complex ML projects from problem definition through production deployment and long-term maintenance.
• Translate business and product requirements into scalable machine learning architectures.
• Drive the complete ML lifecycle, including data strategy, annotation processes, dataset management, experimentation, model development, deployment, monitoring, and continuous improvement.
• Design and build reusable Python libraries, ML frameworks, and internal tooling that improve engineering efficiency.
• Deliver reliable, maintainable, well-tested, and scalable production code.
• Collaborate closely with software engineers, product managers, and business stakeholders to maximize customer value.
• Improve model quality through rigorous experimentation, performance analysis, and data-driven decision making.
• Lead engineering initiatives around reproducibility, testing, CI/CD, observability, MLOps, and deployment best practices.
• Mentor other Machine Learning Engineers through technical guidance, code reviews, and knowledge sharing.
• Contribute to technical architecture decisions and help shape the long-term direction of the ML platform.

What we need to see

• 5+ years of commercial experience building and deploying machine learning systems.
• Strong understanding of machine learning fundamentals, including supervised, unsupervised, and self-supervised learning; optimization; probability and statistics; linear algebra; and model evaluation and validation.
• Deep understanding of the entire machine learning lifecycle and the practical challenges of operating ML systems in production.
• Excellent Python software engineering skills, including clean, maintainable, and testable code; algorithms and data structures; debugging and performance optimization; and reusable libraries and APIs.
• Extensive experience developing deep learning models using PyTorch.
• Experience designing production ML systems with scalability, reliability, and maintainability in mind.
• Strong understanding that successful ML products depend equally on data quality, evaluation methodology, software engineering, and deployment.
• Proven ability to independently lead technical initiatives from concept to production.
• Experience mentoring engineers and providing technical leadership.
• Strong communication skills, including explaining complex technical concepts to technical and non-technical stakeholders.

Ways to stand out

• Experience with Docker, Kubernetes, NVIDIA Triton, ONNX, TensorRT, and object detection.
• Strong experience with MLOps tools and production ML infrastructure.
• Contributions to open-source ML projects or technical publications.
• Extensive experience building production computer vision systems.
• Experience optimizing model inference, latency, and deployment efficiency.
• Experience designing large-scale datasets, annotation pipelines, and data quality processes.
• Experience building internal ML platforms, frameworks, or developer tooling.
• Experience shipping customer-facing AI products in a fast-paced startup environment.

What we offer

• Direct collaboration with company founders using bleeding-edge technology.
• Opportunity to shape the technical direction of the AI platform.
• Exciting technical challenges with real ownership and impact.
• Opportunity to accelerate your career.
• Friendly, start-up office atmosphere.
• Experience in a dynamically growing company.
• Fully remote work model.
• Fitness and Health membership benefits.
• Gaming laptop for work.
• Company off-sites and team activities.