Senior / Staff ML Ops Engineer (AI)

Location: Hybrid; remote work available in the United States and Canada. Office locations include Dallas, Phoenix, Pittsburgh, San Francisco, and Toronto.

Salary: $184,000–$272,000 USD per year for US locations, plus equity incentives, an annual performance bonus, and benefits.

Company

hirify.global is a technology startup building Physical AI and autonomous transportation systems for commercial autonomous trucks and robotaxis.

What you will do

• Build and evolve Kubernetes-based ML training infrastructure, including GPU scheduling, autoscaling, distributed jobs, capacity planning, operators, and workflow engines.
• Develop developer-facing CLIs, SDKs, job submission flows, templates, and self-service paths for research and engineering teams.
• Improve training iteration speed through local workflows, fast failure detection, platform measurement, and reduced time to first training run.
• Strengthen dataset versioning, sharding, high-throughput multimodal sensor-data loading, experiment tracking, model registries, and lineage.
• Build CI/CD, observability, access controls, data-handling guardrails, and cost governance across the ML platform.
• Drive adoption through prototyping with users, documentation, onboarding, office hours, and reliable production tooling.

Requirements

• 5+ years of software or infrastructure engineering experience with tools or platforms used by other engineers and production ML or data-intensive systems.
• Hands-on Kubernetes expertise, including GPU scheduling, autoscaling, Helm or equivalent, networking, and cluster debugging under load.
• Excellent Python skills and experience designing developer-friendly APIs and CLIs.
• Practical AWS experience with object storage, IAM, GPU compute, networking, cost management, and infrastructure as code such as Terraform or Pulumi.
• Experience with distributed PyTorch training, experiment tracking, model registries, containers, CI/CD, modern build systems, and large monorepos.
• Ability to influence without authority, collaborate across teams, write clearly, operate autonomously, and work on ML, robotics, or autonomous-systems infrastructure.

Nice to have

• Internal developer platform, research platform, or DevEx experience with demonstrated tool adoption.
• Large-scale distributed GPU training, NCCL, high-performance cluster networking, and collective communication tuning.
• High-throughput LiDAR or camera-data pipelines using Parquet or WebDataset.
• Experience with Argo Workflows, Ray, Flyte, Kubeflow, Slurm, Bazel, simulation infrastructure, or batch evaluation pipelines.
• Background in security- and IP-sensitive environments or open-source ML infrastructure and developer tools.

Culture & Benefits

• Hybrid workplace with flexible hours and work-from-home support.
• Medical, dental, and vision coverage for full-time employees.
• Unlimited vacation.
• Competitive compensation, equity awards, and an annual performance bonus.
• Catered meals when working in the office, snacks, drinks, and regular onsite, offsite, and virtual team events.