Senior ML Platform Engineer — Training & Deployment

Maxinsights Corporation; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Maxinsights Corporation is seeking an ML infrastructure engineer to design, build, and evolve the underlying systems for training workflows, deployment, and production handoff. You will collaborate with research, product, data, and engineering partners to translate needs into maintainable platforms.

The role emphasizes reliability, scalability, observability, and developer experience, with a focus on platform abstractions and lifecycle governance for models, artifacts, and configurations that

Qualifications

• Experience owning production or near-production ML systems.
• Practical PyTorch experience with training workflows and orchestration.
• Understanding of HPC for ML training/serving.
• Knowledge of model lifecycle: data, configs, artifacts, reproducibility.
• Ability to build reliable platform abstractions with visibility for ML practitioners.
• Strong engineering standards: tests, docs, debugging tools.

Responsibilities

• Design and evolve infrastructure for ML training workflows.
• Develop deployment paths for models, jobs, and services.
• Improve reliability, scalability, observability, developer experience.
• Define interfaces and lifecycle boundaries for ML systems.
• Collaborate with research, product, data, and engineering teams.
• Provide operational tooling and safe rollout mechanisms.

Skills

Production systems ownership

PyTorch experience

ML training workflows

Artifact management

Deployment automation

Heterogeneous computing

Model lifecycle concepts

Platform abstractions

Engineering quality

Tools

Docker

CI/CD

Cloud infrastructure

GPUs

Job description

Maxinsights Corporation is seeking an ML infrastructure engineer to design, build, and evolve the underlying systems for training workflows, deployment, and production handoff. You will collaborate with research, product, data, and engineering partners to translate needs into maintainable platforms.

The role emphasizes reliability, scalability, observability, and developer experience, with a focus on platform abstractions and lifecycle governance for models, artifacts, and configurations that