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Our team: Join the team that is building the core infrastructure for AI at Bloomberg.
The Bloomberg AI Inference Platform provides production-grade managed infrastructure for hosting, deploying, and serving all machine learning models, both predictive and cutting-edge generative models.
We abstract away infrastructure complexity, empowering engineering teams to focus on creating intelligent applications with guaranteed scalability, performance, and governance.
Our platform is built on the open-source KServe project, and the CNCS AI Inference team is a primary contributor to its development.
We'll trust you to: Design and build scalable infrastructure for both online and offline inference workloads.
Lead integration of high-performance inference runtimes and serving frameworks, including TensorRT, vLLM, ONNX, and Triton.
Drive architecture and technical decisions across Bloomberg’s inference platform, balancing latency, throughput, reliability, and cost.
Partner across engineering teams to improve model deployment, observability, and production performance.
Mentor junior engineers on system design, debugging, and performance optimization.
You'll need to have: 5+ years of professional software engineering experience.
designing, building, and operating production distributed systems.
Strong systems intuition and a track record of debugging and optimizing performance-critical services.
Ability to own problems end-to-end and quickly ramp up in unfamiliar technical areas. 4+ years of demonstrated experience working with an object-oriented programming language.
A degree in Computer Science, Electrical Engineering, or equivalent practical experience.
We'd love to see: Experience deploying and operating machine learning systems at scale.
with inference optimization techniques such as batching, caching, request scheduling, or memory-aware serving.
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Familiarity with PyTorch and GPU software stacks such as CUDA and NCCL.
Exposure to high-performance interconnects and distributed computing technologies such as NVLink, InfiniBand, or MPI.
with Kubernetes and cloud-native infrastructure.
with load balancing, request routing, or traffic management systems.
Representative projects: Autoscaling a heterogeneous compute fleet to match supply and demand aross diverse inference workloads.
Building production-grade deployment pipelines to safely roll out new models to millions of users.
Developing new inference capabilities such as structured sampling, prompt caching, and advanced serving optimizations.
Analyzing observability data from real production workloads to improve latency, throughput, and resource efficiency.
Global financial software, data, and media company.
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Senior · 5+ years experience
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