Senior AI/ML Engineer (Magnet-Griffeye)

4 months ago

Gothenburg, SwedenSenior

Responsibilities

Own AI/ML initiatives from ideation through experimentation, evaluation, deployment, and handoff for integration.
Design and prototype agentic workflows in which models reason, plan, call tools, and collaborate with other systems.
Train, fine-tune, or adapt models and determine when system design is preferable to larger models.
Develop preprocessing, augmentation, and evaluation techniques for complex datasets while improving model quality and fairness.
Collaborate with engineering, product, UX, research, and design teams to make AI systems production-ready, observable, scalable, and useful.
Contribute to reusable infrastructure for experimentation, evaluation, and deployment.
Embed ethical, responsible, and secure AI practices into system design and deployment.
Mentor engineers on experimental design, evaluation methodology, and technical decision-making.

5+ years of professional experience in ML or applied AI, or equivalent depth demonstrated through delivered work, with production AI/ML delivery experience.
Depth in at least one of model training and evaluation, agentic system design, or retrieval and evaluation architecture, with working familiarity across the others.
Experience evaluating ML/AI systems and designing representative evaluation distributions and meaningful metrics.
Experience with large, complex, or unstructured datasets and trade-offs among model quality, cost, inference speed, and system complexity.
Proficiency in Python and working fluency with modern ML/AI frameworks and tooling such as PyTorch, inference servers, and LLM/agent frameworks.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
Strong communication and cross-functional collaboration skills.
Familiarity with vector databases, embedding models, context retrieval strategies, NLP, computer vision, MLOps tooling, cloud providers such as AWS or Azure, and AI development tools such as Claude or GitHub Copilot is preferred.
Open-source ML/AI contributions or peer-reviewed publications are preferred.

Generous time off policies, volunteer opportunities, reward and recognition programs, employee committees and resource groups, healthcare, and retirement benefits.
Hybrid-flexible work arrangement with occasional in-person attendance, typically weekly to monthly depending on team and department needs.
Competitive compensation and ongoing learning and development support.

501-1,000 employees