Expert Deep Learning Engineer

Insights from the team

At Zenseact, we design and train spatio-temporal neural networks for end-to-end driving — systems that already run in our cars today. Our models are deployed on a single compute platform, processing multi-modal sensor data in real time and making safety-critical decisions on the road. We are a team of engineers and researchers working on the next generation of deep learning for autonomous driving. We work across the full stack: from tailored data pipelines and large-scale training to architecture design, validation and production integration. We train networks from the ground up, using tailored datasets and custom architectures. Collaboration and curiosity drive us forward.

Solving these complex problems is a team effort — researchers and engineers learning from each other, sharing knowledge openly, and growing through hands-on challenges in production systems. Our mission is to create models that understand complex environments and make safe decisions in real time.

The next major release — including the upcoming EX60 in summer 2026 — will further evolve our AI stack in real-world cars. Here, you combine cutting-edge AI with a culture that values collaboration, flexibility and balance — making space for life outside of work while building technology that truly makes a difference on the road.

Your mission and day-to-day tasks

In this role, you will focus on end-to-end learning approaches for autonomous driving. You’ll design spatio-temporal neural networks, train them on large-scale data, and integrate them into production systems. You’ll collaborate closely with your peers – experts in perception, planning, and software engineering, to share ideas and deliver solutions that matter. Together you’re solving the hardest problems in autonomous driving, training AI to predict the unpredictable.

• Develop and optimize deep learning models for end-to-end driving
• Build scalable pipelines for data processing and model training
• Experiment with architectures such as transformers and recurrent networks
• Collaborate across teams to deploy models into real-world scenarios

• Work at the forefront of deep tech in one of the biggest and most interesting, challenging and dynamic AI/ML-companies in Northern Europe.
• Collaborate and team-work with highly skilled, innovative, creative and smart people.
• Work with people from all over the world, close to what science just came up with – or innovate it yourself.
• Enjoy a flexible work approach and a genuinely good work/life balance.

We’re looking for someone who combines technical depth with a sense of responsibility for the bigger picture. You should be analytical, a problem solver, and an active listener who values teamwork and humility.

Essential Experience

• Deep knowledge of spatio-temporal neural networks
• Strong background in deep learning, software development, and data engineering
• Proficiency in Python and frameworks like TensorFlow or PyTorch
• Solid understanding of statistics and geometry

• Transformers, RNNs, multi-head attention
• Perception systems (computer vision, lidar, radar)
• Tracking and sensor fusion
• Experience with imitation learning or behavior cloning

More About Zenseact

Our software makes a difference.

Using AI-based technology to create the ultimate driver support, we’re fighting to end car accidents and make roads safe for everyone. Every year, around 1,4 million people die in traffic while approximately 50 million people get injured. Many get disabled as a result of their injury. We can do better.

One purpose, one product.

We’re a software company dedicated to revolutionizing car safety. By designing the complete software stack for autonomous driving and advanced driver-assistance systems, we’re fighting to end car accidents and make roads safe for everyone. Zenseact was founded by Volvo Cars, and the teams are based in Gothenburg and Lund, Sweden and Munich in Germany. When we aim for zero accidents faster, we strive to speed up the transition to safe automation. This is essentially achieved by making cars updatable – like a computer or a phone. With regular software updates, a vehicle can be made safer long after its production. By accelerating improvement loops, shortening development cycles, and deploying high-capacity software quickly, we can make cars safer, and faster.

Culture with people at heart

To achieve our mission of saving lives and ending traffic accidents we must go where nobody has before. It requires us to venture into the unknown, pioneering new technology and pushing the frontier of autonomous driving. While there’s no denying our determination and expertise, we must stand united to succeed. By fostering a culture of support and enablement – a place of psychological safety where all of us can thrive – everything else will follow. We call this a people-at-heart culture. This culture means caring. It means the company cares about me, and we care about one another. It means sharing, so we give each other energy and have fun together. Our culture is also about belonging. It’s important to feel at home and that we can be ourselves at work. Finally, a people-at-heart culture means well-being. So, we enjoy the flexibility needed to be and do our best – at work and in life.

Zenseact works proactively to create a culture of diversity and inclusion, where individual differences are appreciated and respected. To drive innovation we see diversity as an asset, which means we value and respect differences in gender, race, ethnicity, religion or other belief, disability, sexual orientation or age, etc.

Interviews are held continuously, so we highly recommend that you submit your application at your earliest convenience.

One purpose, one product

We are a software company focused on transforming car safety. By developing a complete software stack for autonomous driving and advanced driver-assistance systems, we aim to eliminate car accidents and make roads safer for all. Founded by Volvo Cars, Zenseact operates globally, with teams in Gothenburg and Lund, Sweden; and Munich, Germany.