Machine Learning Engineer

Acceler8 Talent; Cambridge (MA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Acceler8 Talent is seeking a Member of Technical Staff - ML Engineer in Cambridge, MA, with a hybrid arrangement (three days per week). You will translate research code into reliable services and build scalable ML infrastructure to support training, inference, and experimentation.

Join a small, high-caliber team with backgrounds at NVIDIA and Google, and own critical systems used by researchers and product teams.

Qualifications

• 3+ years operating production ML training or inference infrastructure
• Depth in one area: training, model serving, ML platforms or agent/gateway/evaluation infra
• Experience turning research code into tested services and resolving production failures

Responsibilities

• Turn research code into reliable services and build the ML infrastructure that makes it possible
• Collaborate with researchers and technical leadership to own critical systems
• Diagnose and resolve production issues to maintain reliability and performance

Skills

Production ML infra

Software engineering

Troubleshooting prod failures

Tools

CUDA

Cloud infrastructure

Job description

Member of Technical Staff - ML Engineer

Cambridge, MA | Hybrid, three days a week

What if AI could run physics research and test whether its answers hold up?

We’re hiring for a start up building exactly that, starting with the power and cooling challenges behind AI data centers.

You’ll turn research code into reliable services and build the ML infrastructure that makes it possible. Join a small team with backgrounds including NVIDIA and Google, work directly with researchers and technical leadership, and take early ownership of systems people depend on.

What you need:

• 3+ years operating production ML training or inference infrastructure and building software used by others
• Depth in one area: training, model serving, ML platforms or agent/gateway/evaluation infrastructure.
• Experience turning research code into tested services and personally resolving production failures.

Useful Extras:

• CUDA or ML performance optimization experience.
• Cloud infrastructure experience and experience working within research teams.