Machine Learning Engineer

CNA Search; United States; On-site; USD 170,000 - 230,000; Full time

Job summary

CNA Search is seeking a Senior AI/ML Research Engineer to advance large-scale AI inference, synthetic data generation, and distributed training environments. You will build scalable pipelines, optimize inference performance, and contribute to distributed systems supporting state-of-the-art models.

The role emphasizes hands-on engineering, collaboration with researchers, and publishing research at top conferences.

Qualifications

• Strong AI/ML engineering background with production systems experience.
• End-to-end AI/ML pipeline design and implementation.
• Experience with distributed inference and large-model optimization.
• Hands-on with inference frameworks such as vLLM or SGLang.

Responsibilities

• Design and build large-scale synthetic data generation pipelines and orchestration systems.
• Optimize AI inference workloads for performance, cost, memory, and compute utilization.
• Build and improve distributed inference infrastructure for large-scale AI models.
• Contribute to open-source libraries and frameworks for synthetic data generation and distributed reinforcement learning.
• Research and implement new approaches to model inference, training, and post-training.
• Collaborate with researchers and engineers working on large-scale AI systems.
• Contribute research suitable for publication at conferences such as ICML and NeurIPS.
• Translate highly technical work into clear technical content for developers and users.
• Stay current with advances in AI infrastructure, distributed inference, synthetic data, and model optimization.

Skills

AI/ML engineering

Distributed systems

Inference optimization

Research to production

Open-source collaboration

Tools

vLLM

SGLang

GPU optimization

Job description

We're looking for a Senior AI/ML Research Engineer to work on large-scale AI inference, synthetic data generation, and distributed training systems.

This is a hands-on research and engineering role for someone who has built infrastructure supporting large-scale AI models and wants to work on difficult problems across inference performance, distributed systems, reinforcement learning, and synthetic data.

No third parties

What You'll Do

Design and build large-scale synthetic data generation pipelines and orchestration systems.

Optimize AI inference workloads for performance, cost, memory, and compute utilization.

Build and improve distributed inference infrastructure for large-scale AI models.

Contribute to open-source libraries and frameworks for synthetic data generation and distributed reinforcement learning.

Research and implement new approaches to model inference, training, and post-training.

Collaborate with researchers and engineers working on large-scale AI systems.

Contribute research suitable for publication at conferences such as ICML and NeurIPS.

Translate highly technical work into clear technical content for developers and users.

Stay current with advances in AI infrastructure, distributed inference, synthetic data, and model optimization.

What We're Looking For

• Strong AI/ML engineering background with experience building production systems for large-scale model inference or training.
• Experience designing and implementing end-to-end AI/ML pipelines.
• Deep understanding of distributed inference and optimization of large-model workloads.
• Hands-on experience with inference frameworks such as vLLM or SGLang.
• Strong understanding of GPU compute, memory optimization, throughput, latency, and resource utilization.

Experience with one or more of the following is especially valuable: