Overview
We are on a mission to ensure everyone has access to medical expertise, no matter where they are.
Half the world still lacks access to quality healthcare. Even in advanced systems, outcomes are uneven, and clinicians are overwhelmed. Medical knowledge grows faster than human capacity can keep up.
Corti is building the infrastructure to close that gap. Our AI platform expands access to medical expertise, reducing errors, restoring time to clinicians, and making care more affordable, accessible, and human again.
There is no quality healthcare without a quality dialogue, and no reliable AI without a strong foundation. Help us build both.
Why Corti?
Corti is building the intelligence layer for global healthcare. We give every developer, product team, and healthcare innovator access to medical-grade AI, so the world can deliver care that is faster, safer, and more human.
Built entirely for healthcare and adjacent industries, Cortiās models are trained on real-world data and optimized for precision, safety, and regulatory trust.
Through modular APIs, teams can embed medical speech recognition, summarization, reasoning, and much more directly into healthcare products without reinventing the foundation.
We power the builders who are redefining how healthcare works, from startups creating new patient experiences to enterprises modernizing the systems that care depends on.
If you believe that AI purpose-built for medicine will define the next century of healthcare, you belong at Corti.
As a Senior Machine Learning Engineer focused on LLMs and clinical NLP, you will help build and productize large language model capabilities, with emphasis on clinical summarization and medical text generation. You will work in our machine learning R&D group and partner closely with product and platform engineering.
This role includes meaningful ownership across the model lifecycle, from data and task formulation to training, evaluation, serving, and monitoring. The team values strong engineering practices and collaboration, and we aim to build systems that are reliable, measurable, and safe to use in clinical settings.
Build and improve LLM-based clinical NLP systems, including summarization, structured extraction, and controlled generation.
Train, finetune, and post-train LLMs using approaches such as supervised finetuning and preference or feedback-driven optimization where appropriate.
Design evaluation strategies for clinical text generation, including offline benchmarks, human review workflows, slice-based analysis, and quality gates aligned with clinical risk.
Develop and operate LLM inference services using vLLM, with focus on reliability, scalability, and practical performance.
Optimize inference for latency, throughput, and cost, for example batching, caching, quantization, and decoding strategy improvements.
Build and maintain APIs and services using FastAPI, and deploy and run them on Kubernetes.
Take technical ownership of core NLP components, shaping best practices for model development, evaluation, and production reliability across the team, and supporting the growth of engineers working on text generation systems.
Partner with researchers, engineers, and product teams to ship improvements end-to-end, including observability and monitoring to support continuous iteration.
PyTorch, as well as post-training frameworks such as TRL/Axolotl/Unsloth
vLLM for LLM serving
Kafka and FastAPI for ML APIs and services
Kubernetes for deployment and operations
Common MLOps tooling for experiment tracking, model versioning, and monitoring
Strong programming skills in Python and the ability to contribute to production-grade codebases.
Hands-on experience with LLMs for NLP or text generation, including at least some of the following:
Training, fine-tuning, or post-training transformer-based models.
Building or operating LLM inference services in production, including performance work.
Designing robust evaluations for generative systems, including metrics, error analysis, and human evaluation methods.
Experience turning research outcomes into practical systems that can be validated and shipped.
Familiarity with building ML systems beyond notebooks, such as data pipelines, CI/CD practices, monitoring, and deployment workflows.
Clear communication and collaboration skills across research, engineering, and product.
A Masterās degree in computer science, engineering, mathematics, statistics, physics, or a related field, or equivalent professional experience.
Nice to have: experience with healthcare data, clinical NLP, privacy and safety considerations, or working with domain experts in evaluation.
You will be reporting to the Director of Engineering
The position is full-time and starts as soon as possible
Hybrid working environment in our Copenhagen Office
Equipment provided by Corti
Ready to dive into the world of Corti? Hit that 'Apply' button, and let's start working together on reshaping the dialogue in healthcare, making a real difference for millions of patient outcomes around the world.
š¤ Bringing in top talent from all backgrounds is crucial in our pursuit to improve the world of healthcare. We encourage applications from all people and do not discriminate based on race, religion, national origin, gender, sexual orientation, age, and/or disability status.ļ»æ
At Corti, experience comes in many forms, and weāre passionate about creating teams with a multitude of perspectives! If you believe your experience is close to what weāre looking for but not an exact match, we still hope youāll consider applying!
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