Senior Staff Perception Engineer

Senior Staff Perception Engineer (Autonomous Systems)

Remote across Europe / US

Highly competitive salary + equity

The company and opportunity

Akkar is partnering with a well-funded autonomy company building advanced self-driving technology for complex real-world environments.

The company has moved beyond prototypes and research demonstrations, with autonomous systems already being deployed into live operational settings. As the platform continues to scale, they are expanding the team responsible for how those systems perceive and understand the world around them.

This is a senior technical position with significant influence over the architecture and direction of the perception stack.

You will remain hands-on technically, while also helping define how the perception system evolves across sensing, machine learning, data, evaluation and production deployment.

What you will do

• Architect and develop 2D and 3D perception systems using sensors including camera, LiDAR and radar.
• Work across the wider perception stack, including sensing, preprocessing, detection, tracking and multimodal fusion.
• Develop and deploy machine-learning models that operate reliably in real-world environments.
• Improve the data collection, dataset generation and evaluation infrastructure supporting the perception system.
• Investigate system limitations and identify where new models, architectures or tooling can improve performance.
• Help shape the longer-term technical roadmap for perception.
• Lead technically complex projects across perception and the wider autonomous systems organisation.
• Mentor engineers and contribute to engineering and ML best practices across the team.

What you will bring

This role is aimed at someone with substantial experience developing perception systems for autonomous vehicles, robotics or another real-world autonomous platform.

You should have strong experience across 2D/3D object detection, tracking and sensor-based perception , with evidence of taking systems beyond offline experimentation and into real-world operation.

We would be particularly interested to talk to you if you have:

• Significant industry experience building production perception systems.
• Strong knowledge of camera, LiDAR, radar or multimodal perception.
• Experience architecting systems rather than only developing individual models.
• Strong Python and modern machine-learning experience.
• An understanding of the wider perception lifecycle, from sensors and data through training, evaluation and deployment.
• Experience diagnosing real-world failure modes and improving system robustness.
• The ability to provide technical direction while remaining hands-on.

Useful additional experience

Experience from autonomous driving, robotics, mobile robotics or other safety-critical autonomous systems would translate particularly well.

Additional useful experience could include:

• Deep learning for 3D perception.
• Multi-object tracking.
• Sensor fusion.
• Large-scale perception datasets.
• ML deployment and optimisation.
• C++.
• Research publications within computer vision, robotics or machine learning.

A research background is useful, but this is fundamentally a role for someone who has seen perception systems interact with the physical world.

How you like to work

You are comfortable owning technical problems where there is no obvious solution.

You can move between architecture, model development, data, evaluation and deployment depending on what is currently limiting the system.

At the same time, you are comfortable operating at Staff level: challenging existing approaches, influencing technical decisions and helping other engineers solve difficult problems.

The opportunity should appeal to someone who wants to help shape an autonomous platform rather than own one isolated part of a perception pipeline.

Interested?

Apply with your CV and, where possible, highlight a perception system you have personally architected, deployed or significantly influenced.

It would be particularly useful to understand the sensors involved, which parts of the system you owned and the challenges you encountered once the technology moved into real-world operation.