Remote ML Infra Engineer for Large-Scale AI Training

true-talent-solutions-partners; San Francisco (CA); Remote; USD 200,000 - 400,000; Full time

Benefits offered by this job

Remote-friendly

Competitive salary

Job summary

true-talent-solutions-partners is hiring an ML infrastructure engineer to design, deploy, and maintain distributed training and inference clusters for large-scale models. You will build scalable pipelines for petabyte-scale datasets and optimize performance across the ML lifecycle.

The role requires deep GPU expertise (CUDA/JAX), familiarity with FSDP/DeepSpeed, and cloud/container tech (GCP/AWS/Azure, Docker, Kubernetes). This remote-friendly position spans the US, with SF as a primary locale.

Qualifications

• 2–10 years of experience building large-scale ML infrastructure for pretrained or foundation models.
• Experience optimizing training, data, or inference workloads, including low-level GPU work with CUDA and JAX.
• Proficiency with distributed training frameworks such as FSDP or DeepSpeed.
• Experience with cloud platforms (GCP/AWS/Azure) and containers/orchestration (Docker/Kubernetes).
• Familiarity with scalable model serving, distributed task management, observability, and version control practices.
• Background in physical AI or AI for science and willingness to work across the ML lifecycle.

Responsibilities

• Design, deploy, and maintain distributed ML training and inference clusters.
• Build scalable end-to-end pipelines for petabyte-scale datasets and model training.
• Evaluate parallelization methods and numerical precision trade-offs at different model scales.
• Profile and debug low-level GPU operations to improve performance.
• Track relevant research and apply ideas to infrastructure and workflows.

Skills

Distributed training infrastructure

Model serving

Observability & monitoring

GPU performance tuning

Learning unfamiliar domains

Tools

CUDA

JAX

FSDP

DeepSpeed

Docker

Kubernetes

GCP

AWS

Azure

Job description

true-talent-solutions-partners is hiring an ML infrastructure engineer to design, deploy, and maintain distributed training and inference clusters for large-scale models. You will build scalable pipelines for petabyte-scale datasets and optimize performance across the ML lifecycle.

The role requires deep GPU expertise (CUDA/JAX), familiarity with FSDP/DeepSpeed, and cloud/container tech (GCP/AWS/Azure, Docker, Kubernetes). This remote-friendly position spans the US, with SF as a primary locale.