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 @ 4
NLP
PyTorch @ 4
RAG
Statistics @ 4
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 has more than 400 AI practitioners building products and features using transformers, gradient boosted decision trees, large language models, and dense vector databases. The team develops AI-driven, customer-facing products for searching, discovering, and working with financial news, research, data, and analytics across more than 35 million financial instruments.
The role focuses on Large Language Modeling research and applications for Bloomberg's NLP capabilities. Potential areas include LLM application and fine-tuning methods, efficient training, multimodal models, learning from feedback and human preferences, retrieval-augmented generation, summarization, semantic parsing and tool use, financial-domain adaptation, dialogue interfaces, LLM evaluation, model safety, and responsible AI.
Responsibilities
- Collaborate with colleagues to build and apply LLMs for production systems and applications.
- Write, test, and maintain production-quality code.
- Train, tune, evaluate, and continuously improve LLMs using large amounts of high-quality data to develop state-of-the-art financial NLP models.
- Demonstrate technical leadership by owning cross-team projects.
- Stay current with research in AI, NLP, and LLMs 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 and familiarity with Machine Learning, Deep Learning, and Statistical Modeling techniques.
- Ph.D. in Machine Learning, Natural Language Processing, or a relevant field; or an MSc in Computer Science, Machine Learning, Mathematics, Statistics, Engineering, or a related field with at least 2 years of relevant work experience.
- Experience with Large Language Model training and fine-tuning frameworks such as PyTorch, Hugging Face, or DeepSpeed.
- Proficiency in software engineering.
- Understanding of Computer Science fundamentals, including data structures and algorithms, and a data-oriented approach to problem-solving.
- Excellent communication skills and the ability to collaborate with engineering peers and non-engineering stakeholders.
- 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, and vision coverage, short- and long-term disability benefits, a 401(k) match, life insurance, and wellness programs. Bloomberg also offers additional total rewards and benefits subject to applicable employment conditions.
Salary range: 165,000–260,000 USD annually, plus benefits and bonus. Actual compensation may vary based on geographic location, work experience, market conditions, education or training, and skill level.