Senior Machine Learning Engineer, ML Infrastructure- Online (Machine Learning)

Location: Remote, Washington, USA

Base salary: $165,600–$273,400 annually, depending on geographic zone and qualifications.

Company

hirify.global develops a leading game engine and 3D development platform used across games, XR, web, automotive, manufacturing, and healthcare.

What you will do

• Design and operate large-scale online inference infrastructure for production machine learning models.
• Build infrastructure for distributed training workflows using PyTorch, Ray Data, and Ray Train.
• Integrate machine learning pipelines with workflow orchestration systems such as Flyte and Airflow.
• Optimize inference through dynamic batching, model compilation, GPU/CPU utilization improvements, kernel fusion, request scheduling, and runtime tuning.
• Improve observability, reliability, reproducibility, model packaging, artifact validation, compatibility testing, and deployment automation.
• Lead architectural improvements and partner with ML engineers, platform teams, and product stakeholders on safe, scalable, and cost-efficient model iteration.

Requirements

• Experience building and operating production-grade online ML inference systems and model-serving frameworks such as NVIDIA Triton Inference Server, TorchServe, Ray Serve, or TensorFlow Serving.
• Strong experience with distributed systems, Kubernetes, autoscaling, service reliability, and production observability.
• Strong Python programming skills and practical experience with production ML systems and high-scale services.
• Experience with PyTorch, model packaging, validation, deployment workflows, safe rollouts, canary testing, A/B experimentation, and automated rollback.
• Ability to reason about latency, throughput, reliability, scalability, and cost tradeoffs and influence architectural decisions across teams.
• Professional English proficiency is required for frequent communication with colleagues and partners worldwide.

Culture & Benefits

• Health, life, and disability insurance options, with eligibility varying by country and employment status.
• Employee stock ownership and competitive retirement or pension plans.
• Generous vacation and personal days, plus parental leave and family-care programs.
• Mental health and wellbeing support, employee resource groups, and a global employee assistance program.
• Training and development programs, volunteering support, and donation matching.