Member of Technical Staff - ML Engineer

Acceler8 Talent; Cambridge (MA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Acceler8 Talent in Cambridge, MA is seeking a Member of Technical Staff – ML Engineer to design and build scalable ML training and inference infrastructure in a hybrid setting.

You will develop distributed training systems, GPU scheduling and model-serving services, turning research code into reliable production-ready services and tools that support AI agents and model evaluation. You will troubleshoot complex issues and drive performance improvements.

Qualifications

• 3+ years of experience building and operating production ML infrastructure.
• Strong background in distributed systems, ML platforms, model serving or training infrastructure.
• Experience developing systems and tools used by other engineers or researchers.
• Proven ability to diagnose and resolve complex production issues.
• Familiarity with GPU computing, cloud infrastructure or ML performance optimisation is beneficial.

Responsibilities

• Design and build scalable ML training and inference infrastructure.
• Develop distributed training systems, GPU scheduling and model-serving services.
• Turn research code into reliable, production-ready services and tools.
• Build infrastructure supporting AI agents, model evaluation and experimentation.
• Troubleshoot complex infrastructure failures and improve system performance and reliability.

Skills

Distributed systems

Production ML

Model serving

GPU computing

Cloud infrastructure

Tools

Kubernetes

Docker

GPU scheduling

Job description

Member of Technical Staff – ML Engineer | Cambridge, MA (Hybrid)

I'm working with an early-stage AI company building the infrastructure needed to support advanced AI research and scientific discovery.

They're looking for experienced ML Engineers who can bridge the gap between research and production, building reliable systems that allow researchers to train, deploy and evaluate models at scale.

The role:

• Design and build scalable ML training and inference infrastructure
• Develop distributed training systems, GPU scheduling and model-serving services
• Turn research code into reliable, production-ready services and tools
• Build infrastructure supporting AI agents, model evaluation and experimentation
• Troubleshoot complex infrastructure failures and improve system performance and reliability

What they're looking for:

• 3+ years of experience building and operating production ML infrastructure
• Strong background in distributed systems, ML platforms, model serving or training infrastructure
• Experience developing systems and tools used by other engineers or researchers
• Proven ability to diagnose and resolve complex production issues
• Familiarity with GPU computing, cloud infrastructure or ML performance optimisation is beneficial

Why consider it?

• An opportunity to work at the intersection of AI research and large-scale systems engineering, building the infrastructure that enables researchers to develop and evaluate increasingly sophisticated AI models.
• You'll work closely with AI researchers and engineers, taking ownership of technically challenging infrastructure problems with a direct impact on research capabilities.

Location:

Cambridge, MA | Hybrid (3 days onsite)