ML Systems Engineer: Scalable Training & Realtime Inference

Jobzhr; New York (NY); On-site; USD 180,000 - 280,000; Full time

Job summary

Jobzhr, a leading global trading firm, is expanding its ML infrastructure in New York. We are hiring engineers to build distributed training and low-latency inference systems that move models from research into production.

You will work closely with researchers and traders, operating in a fast-paced, IC-heavy environment near the trading floor. You should have 3+ years of hands-on experience with PyTorch, distributed training, and production-grade ML pipelines.

Qualifications

• Build and scale distributed training systems for large models.
• Develop low-latency inference pipelines to production.
• Collaborate directly with researchers and traders to deploy models.
• 3+ years of relevant experience; IC-heavy role with hands-on work.

Responsibilities

• Design and implement scalable training infrastructure for multi-GPU/multi-node setups.
• Create production-ready inference pipelines with low latency.
• Collaborate with researchers and traders to deliver end-to-end systems.
• Manage growing GPU footprint and ensure reliability in production.

Skills

PyTorch

Distributed systems

Performance engineering

Production ML

Collaboration with researchers and qun

Education

Bachelor's degree in Computer Science or related field

Tools

CUDA

Docker

Job description

Jobzhr, a leading global trading firm, is expanding its ML infrastructure in New York. We are hiring engineers to build distributed training and low-latency inference systems that move models from research into production.

You will work closely with researchers and traders, operating in a fast-paced, IC-heavy environment near the trading floor. You should have 3+ years of hands-on experience with PyTorch, distributed training, and production-grade ML pipelines.