Member of Technical Staff - ML Engineer

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

Job summary

Acceler8 Talent partners with an early‑stage AI company in Cambridge, MA, building infrastructure for advanced AI research and discovery. The role bridges research and production, focusing on scalable ML training and inference systems in a hybrid setting.

As a Member of Technical Staff – ML Engineer, you design distributed training, GPU scheduling and model‑serving services, turning research code into robust production tools for researchers to train, deploy and evaluate models at scale in

Qualifications

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

ML platforms

Model serving

Training infrastructure

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)