Computer Vision Engineer
Role details
Job location
Tech stack
Job description
As a part of our Engineering team, you will work on developing models and systems to solve complex Safety issues in complex Industrial environments. You will develop new models for new use cases and refine existing designs to accommodate changing behaviours. You will bring the SOTA models, techniques and guidelines to the team to keep up with the rapidly changing ML world., * Have a strong sense of ownership and accountability. You don't wait to be told or until things are clearly defined. You exemplify resourcefulness, and a bias for action.
- You're driven by curiosity about how things work, and how to make them better. While you're an expert, you're humble and open to feedback.
- Are a clear communicator, both async and in-person. You thrive in bringing clarity from ambiguity.
- You're a great collaborator, working cross-functionally, with engineers, PMs, and customers
What You'll Get
- High ownership, high impact role in a purpose-driven, high-growth startup.
- Opportunity to shape the machine learning function.
- Berlin based hybrid setup with flexibility for focus work.
- A committed team environment where your growth is a priority.
- The chance to directly influence industrial safety and help employees go home safely every day.
Requirements
Do you have experience in Python?, * 5+ years of experience delivering software services powered by machine learning
- Experience working with cloud infrastructure such as AWS, GCP or Azure
- Fluent in Python;
- Demonstrated experience managing the technical direction of a software system
- Experience in coaching and mentoring senior engineers
- Hands-on experience with modern ML frameworks (e.g., PyTorch)
- Proven experience delivering ML/CV models to production environments, including: Model compilation & optimization (e.g., TensorRT, ONNX Runtime) for accelerated inference. Containerization using Docker for reproducible and scalable deployments
- Knowledge of MLOps concepts and tools for data labeling, experiment tracking, pipeline orchestration, CI/CD, and production monitoring. Eg: LabelStudio, WandB, Grafana, GitHub Actions, MLflow, Docker.
- Solid understanding of the full ML model lifecycle including feature engineering, model training & evaluation, deployment, diagnostics, and ongoing monitoring.
- Experience building scalable data pipelines for ML-based data processing
- Experience developing and maintaining services under SLA
- Analytical mindset with a problem-solving approach
- Passion for solving customer challenges quickly while making thoughtful architectural decisions
- Adaptability and resilience when facing new or ambiguous challenges
- Ability and willingness to work in a hybrid capacity from our Berlin office 3 days a week