Senior Machine Learning Platform Engineer (AI)

Location: New York, NY; 4 days per week in the NYC office, Flatiron District

Salary: $170,000–$220,000 per year in target base compensation at the commencement of employment.

Company

hirify.global provides personalized virtual behavioral healthcare for people with complex mental health, substance use, and eating disorder needs.

What you will do

• Define the technical direction for ML and AI infrastructure, including build-versus-buy decisions.
• Design and operate multi-vendor AI infrastructure and production model-serving systems.
• Build GPU inference pipelines, infrastructure as code, training pipelines, and deployment workflows.
• Own multimodal data pipelines for text, audio, and structured clinical data.
• Create orchestration, evaluation, monitoring, alerting, observability, and debugging tools for agentic AI and LLM applications.
• Partner with data scientists, ML engineers, and product teams while participating in on-call support for platform reliability.

Requirements

• 4+ years of professional software engineering experience, including at least 2 years in ML infrastructure, ML platform, or AI systems engineering.
• Strong Python software engineering fundamentals and deep infrastructure expertise.
• Experience with cloud ML services such as AWS SageMaker or GCP Vertex AI and CI/CD for ML pipelines.
• Experience with infrastructure as code, including Terraform or Pulumi, and container orchestration with Kubernetes or ECS.
• Ability to manage ambiguity, decompose complex problems, iterate clearly, and collaborate across engineering, product, and design.

Nice to have

• Experience building evaluation and observability systems for LLM-based or agentic AI applications.
• Experience with Go, Rust, or C++.

Culture & Benefits

• Comprehensive benefits for full-time, exempt employees.
• Potential stock options and other company-sponsored benefits.
• Values centered on connection, curiosity, evidence, urgency, and persistence.
• Culture of collaboration, learning, mentoring, documentation, and knowledge sharing.