Senior Software Engineer - AI Inference New York, NY Posted yesterday

Bloomberg; New York (NY); Hybride; USD 160 000 - 240 000; Plein temps

Avantages offerts par ce poste

Medical benefits

401(k) with employer match

Paid time off

Résumé du poste

Bloomberg is seeking a Senior Software Engineer for AI Inference in New York. You will help design and operate the Bloomberg AI Inference Platform, delivering scalable, low-latency inference for models, both online and offline.

You will mentor engineers, drive architecture decisions, and collaborate across teams to optimize deployment, observability and performance at scale.

Qualifications

• 5+ years of professional software engineering experience.
• Experience designing, building, and operating production distributed systems.
• Strong systems intuition and debugging of performance-critical services.
• Ability to own problems end-to-end and ramp in unfamiliar areas.
• 4+ years of demonstrated experience with an object-oriented language.
• Degree in Computer Science, Electrical Engineering, or equivalent.

Responsabilités

• Design and build scalable infrastructure for online and offline inference workloads.
• Lead integration of high-performance inference runtimes and serving frameworks.
• Drive architecture decisions balancing latency, throughput, reliability and cost.
• Collaborate with teams to improve model deployment, observability and production performance.
• Mentor junior engineers on system design, debugging and performance optimization.

Connaissances

Distributed systems

Performance debugging

OO programming

End-to-end ownership

Mentoring

Formation

Bachelor's degree in CS/EE or equivalent

Outils

Kubernetes

PyTorch

CUDA/NCCL

Triton

ONNX

Description du poste

Senior Software Engineer - AI Inference

Location

New York

Business Area

Engineering and CTO

Ref #

10050779

Description & Requirements

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.
• 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.
• Experience with inference optimization techniques such as batching, caching, request scheduling, or memory-aware serving.
• 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.
• Experience with Kubernetes and cloud-native infrastructure.
• Experience 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.

Salary Range = 160,000 - 240,000 USD Annual + Benefits + Bonus

The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.

We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.

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Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law.

Bloomberg is a disability inclusive employer. Please let us know if you require any reasonable adjustments to be made for the recruitment process. If you would prefer to discuss this confidentially, please email [e-mail]