Bloomberg

Bloomberg’s internal and enterprise compute and data science solutions were established to support development efforts around data-driven compute, machine learning, and business analytics. Both the Data Science Platform and BQuant Platform are solutions that aim to provide scalable compute, specialized hardware and first-class support for a variety of workloads such as Spark, Trino, and Jupyter. These solutions are built using containerization, container orchestration and cloud architecture.

As the needs of distributed compute, machine learning, data exploration and analysis advance, so do the needs of the compute solution that underscores it. Accentuated by the widespread success of Large-Language-Models and AI initiatives across Bloomberg, these platforms are poised for continued growth to accommodate the endless number of products across Bloomberg that rely on a robust compute environment. Highlights from our upcoming roadmap focus on creating a highly scaled and performant compute solution that abstracts away common requirements that appear across many use cases, including creating a highly available federation layer for Batch Spark Workloads, increasing compute resource usage efficiency and visibility, enhancing the Interactive Spark experience, and continuing to enhance our cloud integration within BQuant’s infrastructure.

As a member of the Spark Engineering Team, you’ll have the opportunity to make key technical decisions to keep these solutions moving forward. Our team makes extensive use of open source (e.g. Spark, Kubernetes, Istio, Calico, Buildpacks, Kubeflow, Jupyter etc.) and is deeply involved in a number of communities. We collaborate widely with the industry, contribute back to the open source projects, and even present at conferences. While working on the platform, the backbone for many of Bloomberg's up and coming products, you will have the opportunity to collaborate with engineers across the company and learn about the technology that delivers products from the news to financial instruments. If you are a software engineer who is passionate about building resilient, highly available infrastructure and seamless, usable full stack solutions, we'd like to talk to you about an opening on our team.

We’ll trust you to:

• Interact with data engineers and ML experts across the company to assess their development flow and scale requirements

• Solve complex problems such as cluster federation, compute resource management and public cloud integration.

• Build first-class observability in a cloud-native way that provide insights that our users need

• Educate users through tech talks, professional training, and documentation

• 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 who takes charge driving part of Spark’s technical vision

You’ll need to have:

• 4+ years of programming experience with at least 2 object-oriented programming languages (Go, Python, Java) and willingness to learn more as needed

• A degree in Computer Science, Engineering or similar field of study or equivalent work experience.

• Experience building and scaling container-based systems using Kubernetes

• Experience with distributed data analytics frameworks eg. Spark, Trino, Presto, Kafka

• Ability to keep up with open source tech and trends for data analytics

• A passion for providing reliable and scalable enterprise-wide infrastructure

We’d love to see:

• Experience with Kubebuilder and Kubernetes operator-based frameworks

• Experience working with platform security standards such as Spiffe and Spire

• Experience with mainstream machine learning frameworks such as PyTorch, Tensorflow

• Open source involvement such as a well-curated blog, accepted contribution, or community presence

• Experience operating production systems in the public cloud e.g. AWS, GCP, or Azure

• Experience with configuration management systems (e.g. Babka)

• Experience with continuous integration tools and technologies (Jenkins, Git, Chat-ops)

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

• Managing Multi-Cloud Apache Spark on Kubernetes https://bburl/hXNER

• Scaling Spark on Kubernetes - https://bburl/e8ZnC

• Kubeflow for Machine Learning: https://bburl/uJnFT

• HDFS on Kubernetes: Tech deep dive on locality and security: https://bburl/Ymbcs

• Apache Spark on k8s and HDFS Security: https://bburl/DD6NW

• Machine Learning the Kubernetes Way - https://bburl/E7LVp

• Inference with KFServing - https://bburl/jv8Fv

• ML at Bloomberg - https://bburl/TuT42

• Introducing KFServing - https://bburl/XaBz5

• Kubernetes on Bare Metal - https://bburl/B6HXS

• Serverless Inferencing on Kubernetes - https://bburl/Xvqq3

• Serverless ML Inference https://bburl/bECkj