Applied Data Scientist — Medical AI & Model Fine Tuning

Overview This role is for an engineer who wants to work at the forefront of medical AI—not by simply integrating existing services, but by owning the models themselves. You will be responsible for the complete model lifecycle, from raw clinical data to fine-tuned models deployed within real clinical workflows at scale. The work focuses on adapting foundation models to the requirements of healthcare using modern fine-tuning techniques such as LoRA, PEFT, and supervised fine-tuning (SFT). Few teams operate at the intersection of frontier model development and applied medicine, and the impact of your work will be measured through improved clinical workflows, reduced clinician burden, and better patient outcomes. Prior hands-on fine-tuning experience is not a prerequisite. We are looking for strong applied AI engineers with solid fundamentals and the motivation to develop deep expertise in model training and fine-tuning. The right candidate will be supported in growing into the frontier of this work.

About Clinvvo Clinvvo is a Swedish healthtech SaaS platform built by clinicians and technologists. We help healthcare providers digitize care delivery using AI to streamline clinical workflows, improve patient experiences, and reduce costs. Already trusted by leading healthcare providers across Sweden, Clinvvo is rapidly expanding across Europe and beyond. Joining at this stage means contributing directly to the product, technical roadmap, and future of the company as we scale.

Responsibilities You will own Clinvvo’s AI capabilities end to end across the complete lifecycle:

• Data preparation: Collect, clean, curate, and structure clinical datasets to a standard suitable for model training. High-quality fine-tuning begins with high-quality data, and you will own this process.
• Model training and fine-tuning: Adapt LLMs and generative models for medical use cases using LoRA, PEFT, SFT, and related techniques. Design and run experiments, interpret evaluation results, and iterate continuously.
• Production deployment: Take models from experimentation to robust, monitored, production-grade services that clinicians can depend on at scale.
• Evaluation and iteration: Build evaluation frameworks, monitoring systems, and feedback loops that enable models to improve continuously after release.
• Cross-functional collaboration: Work closely with engineers, clinicians, and customers to ensure the solutions you build deliver tangible and measurable clinical and business value.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field
• 3+ years of experience in AI/ML, with a focus on Generative AI applications
• Strong hands-on experience with LLMs, prompt engineering, and structured outputs
• Hands-on experience with speech-to-text systems such as Whisper and audio-processing pipelines
• Proficiency in Python, including pandas, NumPy, and ML/AI libraries
• Proficiency in SQL
• Familiarity with cloud AI services, preferably AWS services such as S3, SageMaker, and Bedrock
• Understanding of LLMOps and MLOps, including evaluation, monitoring, deployment, and iteration
• Proven experience shipping production-grade AI systems at scale
• Strong ownership, clear communication, and the ability to work independently in a fast-paced environment

Good to Have The following skills are advantageous but not required. Candidates with strong fundamentals and the motivation to develop these capabilities are encouraged to apply.

• Experience with LoRA, PEFT, SFT, or other parameter-efficient fine-tuning techniques
• Familiarity with fine-tuning tools and ecosystems such as Hugging Face Transformers, PEFT, TRL, Axolotl, or Unsloth
• Experience with RLHF, DPO, or other preference-alignment methods
• Experience building end-to-end GenAI workflows using LangChain, LlamaIndex, or RAG pipelines
• Familiarity with embeddings, vector databases, and retrieval systems
• Experience with model quantization, distillation, or inference optimization
• Distributed or multi-GPU training experience
• Prior exposure to healthcare, clinical, or other regulated data environments

Why This Role

• Frontier work with real-world impact: Fine-tune models at the cutting edge of AI, with results deployed in real clinical settings.
• End-to-end ownership: Take full responsibility from data preparation and experimentation through to deployment and continuous improvement.
• Growth in a specialized field: Receive meaningful support and investment in developing deep expertise in model training and fine-tuning.
• A meaningful mission: Better software for healthcare translates directly into better care for patients.
• A rapidly expanding company: Join a clinician-founded company at a stage where your contributions will have a visible and lasting impact.
• Clinvvo is building the future of care delivery and the models that will help us get there.