Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI
Algorithms @ 4
Communication @ 6
Data Structures @ 4
Deep Learning @ 3
LLM
Machine Learning @ 3
Mathematics @ 6
NLP
PyTorch @ 4
Statistics @ 6
Technical Leadership
Vector Databases
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Bloomberg's Engineering AI department is expanding its team of Machine Learning and Software Engineers building AI-driven customer-facing products and features. The team develops search, discovery, and workflow solutions using transformers, gradient boosted decision trees, large language models, and dense vector databases.
Bloomberg builds Artificial Intelligence applications that process and organize structured and unstructured information, uncover signals, and produce analytics for financial instruments across global capital markets.
The role focuses on Natural Language Processing research and applications, including information extraction, entity recognition and disambiguation, topic classification, sentiment analysis, text analytics, summarization, event detection, conversation mining, dialogue systems, semantic parsing, question answering, and sentence representations.
Responsibilities
- Collaborate with colleagues on production systems and write, test, and maintain production-quality code.
- Design, train, experiment with, and evaluate NLP models, algorithms, and solutions.
- Demonstrate technical leadership by owning cross-team projects.
- Stay current with the latest NLP research and incorporate new findings into models and methodologies.
- Represent Bloomberg at scientific and industry conferences and in open-source communities.
- Publish product and research findings in documentation, whitepapers, or publications at leading academic venues.
Requirements
- Practical experience with Natural Language Processing problems.
- Familiarity with Machine Learning, Deep Learning, and Statistical Modeling techniques.
- Ph.D. in Machine Learning, Natural Language Processing, or a relevant field with 2+ years of relevant work experience; or an MSc in Computer Science, Machine Learning, Mathematics, Statistics, Engineering, or a related field with 5+ years of relevant work experience.
- Understanding of Computer Science fundamentals, including data structures and algorithms.
- A data-oriented approach to problem-solving.
- Excellent communication skills and the ability to collaborate with engineering peers and non-engineering stakeholders.
- Experience with deep learning frameworks such as PyTorch.
- Proficiency in software engineering.
- A track record of authoring publications in top conferences and journals is a strong plus.
Benefits
Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, 401(k) matching, life insurance, and wellness programs.