Research Engineer Computer Vision in Dynamic Crowd Measurement

Closed job - No longer receiving applicants

We are looking to continuously evaluate and improve our existing algorithms to enable stronger crowd analytics for informed decision making.

We aim to provide the best computer vision algorithms and more accurate machine learning models to scale and represent crowded situations.

Our biggest challenges are to develop methods that infer the state of the environment and are simultaneously accurate, sufficiently complex but computationally fast. This trade of is hard to handle, so we are constantly innovating and developing new and better way of solving the underlying problem of crowd dynamics modelling from observational data.

Job functions

We are looking for a research engineer, who is able to analyse and work on existing computer vision and regression algorithms by quantifying their accuracy and suggest improvements.

This research engineer will have to debug and improve python and c++ code, mainly regarding computer vision related algorithms including detection and tracking, such as kalman filtering or similar. It will be required to work in a scrum (agile) environment, where the scrum master will determine the tasks related to programming each week and progress will be measured on the completion of this objectives.

This engineer will work as part of the engineering team of DCM and will be responsible for conducting quantitative analysis of the output of the algorithms with enough supervision from a senior engineer.

Qualifications and requirements

We are seeking an engineer with experience in computer science, mathematics, statistics and machine learning. The applicant must have knowledge in computer vision, regression methods and quantitative analysis. A good candidate must have very good programming skills, both in Python and C++ being able to actively debug and write efficient - production-ready code.

The applicant must be able to work proactively as part of a team of engineers, achieving meaningful results with weekly supervision.

Conditions

Fully remote You can work from anywhere in the world.

Flexible hours Flexible schedule and freedom for attending family needs or personal errands.

Remote work policy

Fully remote

Candidates can reside anywhere in the world.