Bloomberg runs on data. It's our business and our product. From the biggest banks to the smallest hedge funds, financial institutions need timely, accurate data to capture opportunities and evaluate risk in fast-moving markets. With petabytes of data available, a platform to transform and analyze the data is critical to our success.
              
Bloomberg’s quant platform, BQuant, enables users to develop sophisticated financial applications on top of Bloomberg’s data and services. Customers are able to programmatically access Bloomberg’s data; build and analyze factors; screen securities for investable ideas; backtest custom trading strategies; and much much more, all through BQuant’s unique portal. Customers can deploy the product pre-configured on their own premises, or buy it as a service from Bloomberg. Our mission: to democratize the industry by providing every participant with advanced quantitative tools that might only be available to certain players.

The BQuant platform has further evolved to also support data-driven science, machine learning, and business analytics in a cloud-native way. Customers are enabled to integrate data science and distributed analytics into their quantitative workflows. To support this, the platform works to provide scalable compute, specialized hardware, and first-class support for a variety of workloads such as Spark, Tensorflow, and PyTorch. The platform was developed to provide a standard set of tooling for addressing the Model Development Life Cycle from experimentation and training to inference. The platform runs on top of Kubernetes thereby leveraging containerization, container orchestration, and cloud architecture on an infrastructure stack composed of open source technology.
   
The platform is poised for enormous user growth this year and has an exciting roadmap in terms of new features as well as improved user experience. That’s where you come in. As a member of the multidisciplinary Enterprise Data Science Infrastructure team, you’ll have the opportunity to make key technical decisions to keep this platform moving forward by enabling data science capabilities in a core enterprise quant product.
  
Our team makes extensive use of open-source (e.g. Kubernetes, Tensorflow, Spark, and Jupyter) and is deeply involved in a number of communities. As part of that, we regularly upstream features we develop, present at conferences, and collaborate with our peers in the industry. We are contributors to the Kubeflow project as well as founding members of the KFServing subproject to standardize ML Inference within the Kubernetes ecosystem. For Spark, we have implemented a scalable and resilient external shuffle service for dynamic resource allocation, a pluggable interface for secure worker creation, and a token renewal service that handles privacy and security across jobs, all in line with our effort to improve security and elasticity for Spark on Kubernetes. Open source is at the heart of our team. It's not just something we do in our free time, it is how we work.

We’ll trust you to:

• Interact with quantitative and data scientists to understand their workflows and requirements to inform the next set of features for the platform
• Design and develop libraries and distributed services to support data science projects within both private and public cloud environments
• Regularly present your work to peers, senior stakeholders (including our CTO), and clients

We’ll expect you to:

• Provide reliable, scalable, and composable infrastructure
• Collaborate across data science teams on proper use/integration of our platform
• Tinker at a low level and communicate your work at a high level
• Research, architect and drive complex technical solutions, consisting of multiple technologies
• Mentor junior engineers and be a strong engineering voice alongside other leaders through advising and driving the platform’s technical vision and strategy.

You’ll need to have: 

• 3+ years’ experience developing infrastructure solutions, preferably within a Data Science Infrastructure group
• Experience with distributed systems eg. Kubernetes, Spark, MPI, TF, PyTorch, Kafka
• Proficiency in one or more languages (Python, Go, C++, Java, Scala, or JavaScript) and willingness to learn more as needed
• B.S., M.S., or PhD in Computer Science, Computer Engineering, or equivalent practical experience

We’d love to see: 

• Experience building and scaling Docker-based systems using Kubernetes, Swarm, Rancher, Mesos
• Experience promoting a quantitative workflow or machine learning model from experimentation to  production
• Experience with Kubebuilder and Kubernetes operator-based frameworks
• Open source involvement such as a well-curated blog, accepted contribution, or community presence
• Experience working with GPU compute software and hardware

If this sounds like you, apply! You can also learn more about our work using the links below:

• Machine Learning the Kubernetes Way - https://www.youtube.com/watch?v=ncED2EMcxZ8
• Inference with KFServing - https://www.youtube.com/watch?v=saMkA4fIOH8
• ML at Bloomberg - https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9810-machine+learning+%40+bloomberg%3a+building+on+kubernetes 
• Introducing KFServing - https://www.youtube.com/watch?v=saMkA4fIOH8 
• Scaling Spark on Kubernetes -https://www.youtube.com/watch?v=GbpMOaSlMJ4
• Kubernetes on Bare Metal - https://www.youtube.com/watch?v=svyuBSsMtxs 
• Serverless Inferencing on Kubernetes - https://arxiv.org/pdf/2007.07366.pdf
• Serverless ML Inference https://www.youtube.com/watch?v=HlKOOgY5OyA 
• Kubeflow for Machine Learning: https://learning.oreilly.com/library/view/kubeflow-for-machine/9781492050117/

Bloomberg is an equal opportunities employer, and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.