Machine learning specialist computer

Machine Learning Specialist (Computer Vision)

Safe Intelligence is on a mission to make AI safe and reliable for anyone to use. In this role, you'll play a leading role in helping customers with their ML validation challenges and in helping drive our product forward with insights on how to build the best validation solutions for high stakes machine vision problems.

Key Focus

• ML based computer vision in high stakes applications in Aviation, Mobility, Robotics, and Edge Devices.
• Object detection and tracking.

Responsibilities

• Work closely with customers and end users to understand their machine vision models and help them assess performance.
• Implement prototypes, use cases, and solutions that apply the methods and tools developed at Safe Intelligence to address user specific problems in deep validation and robustness.
• Conduct experiments to evaluate various approaches and weigh their respective trade offs.
• Coordinate with the research and platform teams to guide future development based on use case specific challenges.
• Contribute to the development of an efficient and scalable package for performing verification and robust learning.

Qualifications

• Previous scientific and engineering contributions to key problems in machine vision, such as object detection or tracking, typically evidenced via first author papers in top computer vision conferences such as CVPR, ICCV, and ECCV.
• In depth experience in training, evaluating and deploying state of the art machine vision models, including standard architectures such as YOLO, Vision Transformer architectures, and EVA.
• Experience talking to stakeholders in these models to understand their requirements and guiding them through what is and is not possible or desirable in a model.
• Familiarity with Python and the packages widely used in data science and machine learning. Developers should be familiar with libraries like NumPy, pandas, scikit learn, TensorFlow, and PyTorch.
• Familiarity with best practice in machine learning workflows and MLOps tools.
• Fluency in validation and evaluation framework and metrics frameworks for machine learning such as Accuracy, Recall, F1, Intersection over Union, and others.
• Experience in the Aviation, Mobility, Edge Devices, and/or Robotics industries.
• Experience on industrial real world deployment of machine learning solutions.

Personal Attributes

• Passionate about helping engineering teams achieve their AI and ML goals.
• Comfortable and energized in a fast paced environment.
• Excited about interacting with others and digging in to help solve their problems collaboratively.
• Technical and constantly in a state of learning.
• Able to communicate clearly and efficiently with a variety of audiences including developers, customers, researchers, partners and executives.
• Fearless in getting "hands on" with technology and execution.
• Knowledgeable about modern software engineering processes.
• Comfortable with ambiguity with a drive for clarity.
• Collaborative with, and respectful of others on the team.
• Honest, straightforward and caring about each other's well being.

Company Statement

We are proud to be an equal opportunity employer and work hard to create an environment where people of diverse backgrounds and life experiences can thrive. Great ideas come from everywhere, and we strive to make it easy for people to express themselves and be heard.

If you think you can bring something special to this role, please apply even if you do not meet all listed criteria. Safe Intelligence is exploring uncharted waters, and finding the right crewmates is important to us.

Compensation & Benefits

Safe Intelligence provides competitive compensation based on role and candidate experience. Company benefits for all roles include stock option benefits, mentoring, learning, and development allowance, regular team social and work events, and flexible and generous holidays.

Location

Safe Intelligence is based in London, UK. We value the ability to work flexibly and remotely at times, but we also believe that regular in office interactions make for a more fulfilling and productive experience.

How to Apply

Find us on LinkedIn and submit for this role. If you have any questions, please feel free to email email protected .