MLOps Engineer, Deep Learning

Would you like to help deep learning engineers spend more time solving meaningful problems and less time managing complex workflows? Are you interested in automation, evaluation, observability, and the practical use of agentic AI? In this role, you will help shape how deep learning development works across Zenseact. You will join a team that connects deep learning development with reliable operations. Your work will support a continuous flow of trained and evaluated models, while giving engineers and decision-makers clearer information about performance. Apply today, no CV needed at first stage, only contact information, we'll cover the rest later.

You will join team DLFlow within our MLOps unit. Our MLOps teams create the systems and workflows that bring deep learning development together, enabling the end-to-end deep learning development; including training, evaluating, and deploying hardware-optimized models into the product code. Zenseact’s deep learning teams explore what needs to be solved and develop new ways to solve it. Our team helps turn those advances into a smooth, repeatable process for training, evaluating, and integrating models. We work closely with deep learning developers and other stakeholders across Zenseact to create an efficient AI factory.

The work sits at the intersection of deep learning and operations. We draw on ideas from CI/CD but adapt them to the needs of machine learning. Our focus is to make development more efficient and help deep learning engineers spend most of their time on deep learning itself. Automation, evaluation, and observability are central to what we deliver.

Your role is to improve the way deep learning models move through development, evaluation, and delivery. You will create reliable workflows that make complex work easier to repeat, understand, and improve. You will also help teams decide which metrics matter and present results in a way that supports sound decisions.

A key part of the role is pushing the boundaries of how agentic AI can be integrated into the development cycle itself, rather than remaining a separate tool developers invoke as needed. You will explore new approaches, test them in practice, assess where they create real value, and turn the most promising ideas into dependable working methods.

• Build and improve automated workflows for training, testing, and evaluating deep learning models.
• Develop observability tools that make model performance easier to understand.
• Turn large sets of metrics into clear insights for engineers and decisionmakers.
• Explore how agentic AI can improve deep learning development processes.
• Apply CI/CD principles to deep learning and machine learning workflows.
• Work with deep learning teams to understand their routines, pain points, and technical needs.
• Discuss priorities and solutions with stakeholders, then help establish sustainable ways of working.

We believe you have practical knowledge of deep learning and understand what a typical development cycle looks like. You know the common pain points and can communicate effectively with deep learning engineers. Your interest goes beyond creating model improvements yourself. You are motivated by building the systems and procedures that help an entire development organization work effectively.

You follow new developments in deep learning and agentic AI, with a focus on what they can enable in practice. You combine curiosity with sound judgement and can recognise which emerging ideas have the potential to create real value. You are comfortable discussing ideas and trade-offs with stakeholders and working together to find practical ways forward.

• A master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
• Practical experience with deep learning and a good understanding of its development workflows and challenges.
• Experience with automation, model evaluation, observability, or related MLOps practices.
• Understanding of CI/CD and devOps concepts and how they can be adapted to machine learning.
• Ability to turn technical results and metrics into clear, decision-friendly information.
• Ability to communicate and work effectively with engineers and other stakeholders.
• Hands-on experience integrating AI agents into software development workflows, beyond the use of coding assistants or other interactive AI tools.
• A track record of introducing new development practices across teams.
• Building automated training, testing, or evaluation pipelines for machine learning models.
• Developing observability or visualization tools for model performance.

This role may have access to sensitive information, trade secrets, and confidential data. As part of the recruitment process, the selected candidate might undergo a background check.

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.

You will work on an engineering challenge that does not come with a ready-made answer. You will have room to bring in lessons from other environments while learning from people who work directly with deep learning every day.

Your contribution will give other engineers more space to focus on their core work. At the same time, you will have the opportunity to help shape how new technology, including agentic AI, is introduced thoughtfully into development processes. You will work across team boundaries and help ideas become reliable practices.