Senior ML Engineer

Role: Machine Learning Engineer

Sector: SaaS / Applied AI / Legal Tech

Location: Sweden

Salary: We have purposely left this open as it varies on experience (rest assured we will not be wasting your time!)

The Opportunity

An established SaaS company operating within the legal tech space is looking for a Machine Learning Engineer to play a key role in the evolution of their AI capabilities.

Having moved beyond their initial offering, the business is now focused on delivering AI-powered workflows to support professional users in high-stakes environments. The next chapter of their product journey involves integrating advanced natural language processing and large language model (LLM) technologies into their platform, enabling more intuitive, accurate, and scalable decision-making for users navigating complex information.

This is a rare opportunity to work on applied LLM challenges that demand both innovation and care, in a domain where reliability and long-term outcomes matter.

The Role

This is a hands-on, product-oriented engineering role. You'll be responsible for building, refining, and scaling ML features that sit at the core of a widely used platform. The work will require creativity, autonomy, and an understanding of how to translate ML capabilities into trusted user experiences.

Key areas of focus include structured interaction with unstructured documents, improving the accuracy of AI-generated insights, and helping internal teams balance innovation with domain risk.

What You’ll Be Doing

• Developing and deploying machine learning solutions, particularly those that use LLMs for document understanding and user interaction.
• Working cross-functionally with product, design, and engineering teams to integrate ML features that are robust, explainable, and valuable to end-users.
• Exploring approaches such as retrieval-augmented generation (RAG), few-shot prompting, and domain-specific fine-tuning.
• Iterating rapidly while ensuring quality and reliability in a high-trust domain.
• Staying current with the evolving landscape of foundation models and bringing forward ideas that could elevate the platform.

Ideal Experience

• Strong background in machine learning or NLP, ideally with experience working with LLMs or document-focused AI systems.
• Demonstrated ability to move from prototype to production in a product-focused environment.
• Fluency in modern ML tooling (e.g., PyTorch, Transformers, Vector DBs, etc.) and a practical grasp of ML ops principles.
• Excellent communication skills able to navigate both technical discussions and product-level thinking.
• Experience dealing with unstructured, domain-sensitive data (e.g., contracts, legal/financial/technical documents) is highly valued.
• Familiarity with collaborative or workflow-based platforms is a bonus, though not essential.

Why This Role Stands Out

• A rare chance to work on real-world LLM challenges with meaningful constraints and clear impact.
• A mission-driven environment where trust, transparency, and accuracy are non-negotiable and deeply respected.
• A product-focused culture that gives ML a seat at the strategy table, not just a bolt-on function.
• Access to senior technical advisors and internal advocates who understand how to deliver complex AI features responsibly.

Process

The interview process is structured to ensure alignment and technical depth, with some flexibility around timelines due to availability:

1. Introductory discussion with the product team
2. Technical deep dive with the engineering lead
3. Extended session with members of the technical team

This is a long-term hire for someone looking to grow alongside a product and team that value substance over flash.