Technical Strategist for AI Research - CTO Office

Bloomberg’s CTO Office is the future-looking technical and product arm of Bloomberg L.P. We envision, design, and prototype the next generation infrastructure, platforms, and applications for the Bloomberg Terminal. Our projects include machine learning-powered products, cloud computing infrastructure and strategy, open source stewardship, generative AI, and more. We are passionate about what we do.

What’s In It For You

Bloomberg is seeking an experienced AI/ML research professional with deep and broad understanding of modern machine learning. This role is well suited to someone who publishes in and helps organize workshops at top machine learning research venues, who has experience shaping the research environment in prior roles, and who enjoys engaging with teams working across the spectrum from foundational research to applied ML and product development.

As a member of the ML Strategy team, you will contribute to Bloomberg’s research agenda through original research, collaboration with academic and industry partners, and engagement with the broader machine learning community. You will be expected to form and share technical perspectives on important research directions and help shape how Bloomberg engages with emerging developments in artificial intelligence and machine learning.

A major focus of this role will be strengthening the systems and processes that support high-quality AI research at Bloomberg. You will help design, streamline, automate, and operate workflows that make rigorous, reproducible research easier to review, prepare, publish, and communicate externally. You will work with researchers, research leadership, Communications, Legal, and others to make the research publication pipeline clearer, more scalable, and more effective.

• Contribute to Bloomberg’s research agenda through original research, academic and industry collaboration, and engagement with the broader machine learning research community.

• Help shape a flourishing AI research ecosystem at Bloomberg by strengthening the processes, norms, and infrastructure that support rigorous, reproducible, externally visible research.

• Track important advances in your areas of machine learning expertise, and help Bloomberg understand their relevance to our products, platforms, research agenda, and clients.

• Represent Bloomberg’s AI research and technical perspective in appropriate external settings, including conferences, workshops, academic collaborations, and partner engagements.

• PhD in AI, machine learning, computer science, statistics, optimization, data science, or a related technical field.

• 5+ years of experience post-PhD in industry, academia, or a research-oriented organization.

• Strong publication record and credible presence in the AI research community.

• Experience with rigorous research review and publication processes, including peer-reviewed submissions, reproducibility practices, or internal review systems.

• Strong judgment about AI research quality, rigor, reproducibility, and responsible external communication.

• Ability to build trust and work effectively across research, product, engineering, communications, legal, and external partner communities.

• Excellent written and verbal communication skills, including the ability to translate technical research context for non-specialist stakeholders.

• Strong organizational skills and an interest in improving complex workflows, systems, and processes.

• Comfort operating in ambiguous environments where processes, responsibilities, and edge cases may need to be clarified or improved over time.

• Strong publication record in top ML venues (e.g., ICLR, ICML, NeurIPS, AISTATS, UAI, KDD, JMLR, TMLR).

• Active participation in the research community through workshop or tutorial organization, conference service, peer review, academic-industry collaborations, or open research infrastructure.

• Experience advancing research excellence by designing, improving, or scaling systems that raise the quality, rigor, reproducibility, fairness, efficiency, or impact of research — whether inside an organization or across a broader research community.

• Research expertise that overlaps with one or more priority areas for this position, including model adaptation, compression, and distillation; inference and serving for large models; long-context and memory-augmented systems; reinforcement learning and learning from feedback; synthetic data and data curation; and modern approaches to time-series modeling.

• A good understanding of financial markets, instruments, and products.

• Exposure to Bloomberg Terminal and/or enterprise data products.