Conigital, formed in 2015 with an initial focus on transport systems that support the rollout of automated vehicles. We are developing the next generation of asset & operations management software that can be applied to any operations domain (e.g. transport, energy, manufacturing) and facilitate significant improvements in efficiency through automation.
Conigital is looking for a Junior Path/Motion Planning Engineer to support ongoing development.
You will have real vehicles running in Coventry for testing. You will work with other team members to solve and integrate work from 3 different continents (Europe, Australia, and Asia). Our team is very diverse, and we always look for people with high quality and good grasping of different domains in robotics and outside robotics. We always urge You to design and give your opinion and request inquisitive, creative minds to solve our challenges.
- Solid theoretical background in the Motion planning problem in one or more areas: Decision making, behavioural planning, MPC for planning and control.
- Solid theoretical background in machine learning and its sub-areas (Deep Learning, Reinforcement Learning) applied in Robotics
- BSc degree in Computer Science, Robotics or related field
- Strong skill in modern C++, ROS 1&2, unit tests and software engineer best practices. Python is an extra but not required.
- Experience working on simulation environment
- 1+ years of industry experience writing production-quality, performance-critical code, and maintaining large codebases is desired
Roles & Duties:
- MSc in Computer Science, Robotics or related field
- Experience working with vehicle interfaces, including CAN or Ethernet network
- Experience in deploying autonomous systems in real-world scenarios (ideally in Autonomous Vehicles but any other filed is acceptable)
- Experience working with one or more MP sub-areas such as: graph or sampling-based planning in high-dimensional state spaces, computational geometry, behavioural planning and decision making, planning under uncertainty (POMDPs etc.), model-predictive planning and control, convex optimization, reinforcement learning, imitation learning, etc.
- Academic research background with publication history in relevant conferences/journals
Job Types: Full-time, Part-time, Apprenticeship, Internship
Contract length: 2-6 months
Part-time hours: 20 per week
- Performance bonus
- Casual dress
- Company events
- Company pension
- Flexible schedule
- Monday to Friday
Ability to commute/relocate:
- Coventry, West Midlands: reliably commute or plan to relocate before starting work (preferred)
- Software Development Occupations: 1 year (preferred)
- Robotics Path Planning: 1 year (preferred)
- Robotics Motion Planning: 1 year (preferred)
- Robot operating system (ROS 1 or ROS 2): 1 year (preferred)
- United Kingdom (preferred)