Sr. Machine Learning Engineer (ML Infrastructure)

Location: Mountain View, California, USA

Base salary: $140,700–$232,300 per year, depending on geographic zone and qualifications.

Company

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

What you will do

• Build and maintain data and feature pipelines for training datasets and ML experimentation.
• Develop infrastructure for distributed training and multi-stage ML workflows.
• Contribute to model serving and deployment infrastructure that moves models reliably into production.
• Improve reproducibility and reliability through monitoring, alerting, dataset validation, and testing.
• Investigate ambiguous issues across pipelines and training systems, identify patterns, and deliver end-to-end improvements.
• Collaborate with ML engineers, researchers, managers, directors, and partner teams while communicating progress and tradeoffs.

Requirements

• Production experience building ML infrastructure, data platforms, or distributed systems.
• Strong Python skills and experience with data-intensive workloads.
• Hands-on experience with ML frameworks such as PyTorch and distributed compute systems such as Ray or Spark.
• Experience with data pipelines, model training workflows, or large datasets outside academic settings.
• Ability to learn new tools quickly, analyze ambiguous problems, and translate findings into practical technical requirements.
• Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related field, or equivalent practical experience.

Nice to have

• Experience with Kubernetes and GCP or AWS cloud infrastructure.
• Experience with observability tools such as Prometheus or Grafana.
• Experience with Airflow, Prefect, or other workflow orchestration systems.
• Experience with model serving frameworks such as Triton, KServe, or Ray Serve.
• Exposure to data lakes, warehouses, streaming systems such as Kafka, feature stores, or experimentation platforms.

Culture & Benefits

• Health, life, and disability insurance, with offerings varying by country and employment status.
• Employee stock ownership and retirement or pension plans.
• Vacation, personal days, parental leave, and family-care programs.
• Mental health support, employee assistance programs, and employee resource groups.
• Training and development programs, volunteering support, commute subsidy, and office snacks.
• Professional communication in English is required for regular collaboration with colleagues and partners worldwide.