Mid Machine Learning Engineer · AI / Computer Vision

Surveily is a start-up on a mission to transform the Health and Safety standards of workplaces around the globe. Our technology focuses on monitoring and improving conditions in some of the most challenging and dangerous environments. By leveraging Machine Learning and Real-time Data, we aim to make a significant difference in the lives of countless employees, ensuring their workday is as safe as possible. Join us and be a part of this noble cause.

We are looking for a Machine Learning Engineer to help build computer vision features that solve real customer problems. You'll work across the full machine learning lifecycle - from understanding business requirements and preparing data to training models, deploying them to production, and continuously improving their performance.

We're building a team of end-to-end ML engineers rather than specialists. That means you'll be expected to contribute wherever the biggest bottleneck is: data, modeling, software engineering, deployment, or product integration.

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

What you'll be doing

• Design and deliver production-ready computer vision features from concept to deployment.

• Translate product and business requirements into practical machine learning solutions.

• Work across the complete ML lifecycle, including data collection, annotation strategy, dataset management, experimentation, model development, evaluation, deployment, monitoring, and iteration.

• Build robust Python tooling and shared libraries that improve the productivity of the ML team.

• Develop reliable, maintainable, and well-tested production code.

• Collaborate closely with software engineers and product stakeholders to deliver measurable customer value.

• Continuously improve model quality using data-driven experimentation rather than research for its own sake.

• Help establish engineering best practices around reproducibility, testing, documentation, and MLOps.

What we need to see

• 3-5 years of commercial experience building machine learning systems.

• Strong understanding of machine learning fundamentals, including:

  • supervised, unsupervised, and self-supervised learning,

  • optimization,

  • probability and statistics,

  • linear algebra,

  • model evaluation and validation.

• Solid understanding of the complete machine learning lifecycle and the practical challenges of bringing models into production.

• Strong Python software engineering skills, including:

  • writing clean, maintainable, and testable code,

  • algorithms and data structures,

  • debugging and performance optimization,

  • designing reusable libraries and APIs.

• Experience developing deep learning models using PyTorch.

• Understanding that successful ML systems depend as much on data quality, evaluation methodology, and deployment as on model architecture.

• Ability to independently own technical problems from definition through production delivery.

• Strong communication skills and the ability to translate business problems into technical solutions.

Ways to stand out

• Experience building production computer vision systems.

• Experience deploying ML models and operating them in production.

• Experience with MLOps tools and practices.

• Experience with Docker, Kubernetes, Triton, or cloud infrastructure.

• Experience improving inference performance and optimizing deployment.

• Experience designing datasets, annotation pipelines, or data quality processes.

• Experience building internal ML frameworks or shared developer tooling.

• Experience shipping customer-facing ML products in a startup environment.

What we offer

• Direct collaboration with company founders using bleeding-edge technology.

• Exciting challenges and a real impact on the company's development.

• Opportunity to accelerate your career;

• Friendly, start-up office atmosphere;

• Gaining experience in a dynamically developing company;

• Fully remote work model.

• Fitness and Health membership benefits.

• Employee Share Options Program.

• Gaming laptop for work.

• Company off-sites and team activities.

If you have more questions, please email us at: [email protected]