As a Machine Learning Research Engineer in Morningstar | Sustainalytics

You are responsible for developing new AI technologies used to assess the Environmental, Social, and Governance (ESG) performance of public and private companies globally.

The technologies you will develop range from information extraction, NLG systems to any number of subfields that you see fit in order to help expand the capabilities of our analysts and automatically extract data.

You will be working together with a team of talented and result oriented research engineers. This team’s key focus is to expand our competencies and learn how to leverage new AI technologies through a constant stream of diverse experiments which will allow you to:

• Develop production-ready ML models that will be deployed to solve real-world problems, helping analysts make decisions faster and more effectively and automatically extract insights from data.

• Create and fine-tune models, adapting them to fit our unique data needs.

• Propose new ML architectures and methodologies tailored to our evolving business requirements.

• Collaborate closely with cross-functional teams (including BI, QA, Architecture, and MLOps) to design, implement, and scale feasible AI solutions.

• Work incrementally in a dynamic environment, delivering solutions that adapt to changing requirements.

• Drive continuous innovation through a stream of diverse experiments and projects that make a real-world impact.

• Lead Proof-of-Concepts (PoCs) to assess the feasibility of new technologies.

Qualifications

• 3+ years of experience in researching, developing, and deploying machine learning technologies, with a strong focus on delivering scalable, production-ready solutions.

• Proven track record in leading end-to-end ML projects, from research and prototyping to deployment and monitoring in production environments.

• Expertise in model training, fine-tuning, and adaptation of open-source models (e.g., Transformers, LLMs), including experience with transfer learning and domain adaptation techniques.

• Experience working with diverse data types (e.g., tabular, text, images), and applying advanced preprocessing, feature engineering, and data augmentation techniques.

• Proficiency in deep learning frameworks such as PyTorch, TensorFlow, with hands-on experience in optimizing model performance.

• Demonstrated ability to collaborate with cross-functional teams, including Business Intelligence (BI), Quality Assurance (QA), Architecture, and MLOps, to align technical solutions with business needs.

• Strong communication and documentation skills, with the ability to articulate complex technical concepts to non-technical stakeholders and document experiments for reproducibility.

Nice to have

• Advanced NLP expertise, including experience with state-of-the-art large language models (e.g., GPT-4, BERT, RoBERTa) and tasks like text summarization or machine translation.
• Familiarity with AutoML frameworks and hyperparameter optimization techniques (e.g. Hyperopt, Ray Tune) to streamline model development.
• Literacy in cloud platforms (e.g., AWS, Azure, GCP)
• Knowledge of explainable AI (XAI) techniques to interpret model predictions and ensure transparency in decision-making processes.
• Experience with data versioning and lineage using tools like MLflow to ensure traceability and reproducibility in the ML lifecycle.

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We’ve found that we’re at our best when we’re purposely together on a regular basis, at least three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.