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Drive ESG Impact with AI Innovation at Morningstar | Sustainalytics
Overview
Salary
No salary declared 😔
Location
Bucharest, Romania - Hybrid
Nomad Friendly?
Expires
Expires at anytime
Organisation summary
Join Morningstar | Sustainalytics, a leading company dedicated to assessing the ESG performance of companies worldwide. Work at the forefront of AI research, helping investors define strategies and encouraging companies to embrace sustainable practices. This is your chance to contribute to meaningful change by advancing new AI technologies within a company committed to promoting a healthier ESG culture.
Role Summary
- Develop cutting-edge AI technologies for ESG assessment.
- Lead machine learning model development and implementation.
- Provide technical leadership and mentorship to the ML team.
- Define ML best practices and contribute to technical strategy.
- Create PoCs to test the viability of innovative technologies.
- Adapt open-source models to unique data sets.
- Design new ML architectures and methodologies.
- Collaborate with cross-functional teams to scale solutions.
Role Requirements
- 5+ years of experience in ML research and development.
- Proven track record leading ML projects.
- Expertise in training and fine-tuning open-source models.
- Proficiency in data handling and experiment documentation.
- Knowledge of NLP, NLG, NLU, and text processing is desirable.
- Familiarity with PyTorch, TensorFlow, and computer vision technologies.
- Openness to exploring diverse AI subfields.
Benefits and Work Environment
- Hybrid work model with remote and in-person collaboration.
- Flexibility to adapt work arrangements as needed.
- Access to global resources and engagement with international colleagues.
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.
Your work will ultimately help investors define their strategy leveraging ESG insights and will push companies to improve how they treat the environment, their social spheres (workforce, stakeholders, consumers, nearby communities), and their governance. As more and more companies move towards this direction we aim to have a far-reaching impact by promoting a healthy ESG mentality through novel AI technologies.
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: Lead the development and implementation of advanced machine learning models. Provide technical leadership and mentorship to the ML engineering team. Define best practices and standards. Contribute to long-term technical vision and strategy. Lead development of PoCs to determine the feasibility of a new technology. Train, fine-tune or adapt open-source models to fit our data. Propose new ML architectures and approaches. Collaborate with ML Ops and Software Engineering teams to help them implement a scalable production version of your PoC.
Qualifications: 5+ years of relevant experience researching and developing Machine Learning technologies. Experience leading ML research & development projects. Experience training / fine-tuning open source models (e.g. Transformers, LLMs). Experience handling and exploring various types of data (from tabular, natural text to images). Experience organising and documenting experiment. Nice to have Experience with NLP, NLG and / or NLU technologies (e.g. Transformers, BERT, GPT). Experience with text processing (e.g. NER, Entity disambiguation). Experience with PyTorch, TensorFlow or other similar frameworks. Basic experience with CV technologies (e.g. ImageNet, SSDs, YOLO). Basic Experience with other ML techniques like DecisionTrees, XGBoost, Gradient Boosting. Any AI related interests / experience is welcomed (e.g. Reinforcement Learning, Meta Learning, Symbolic AI).
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.
