Staff Machine Learning Engineer (AI)

Location: Hybrid role requiring four days per week in the New York City office, Flatiron District; the team is entirely based in NYC.

Salary: $175,000–$255,000 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

• Design, train, and evaluate machine learning models and AI systems that improve business and clinical outcomes.
• Lead ambiguous, high-impact ML and AI initiatives from conception through production.
• Develop algorithms and automated solutions with clinical, design, data science, and product partners.
• Establish responsible practices for generative AI and ML model evaluation, retraining, and experimentation.
• Use ML infrastructure to serve real-time model inferences from live data streams.
• Mentor engineers and strengthen collaboration, documentation, presentations, and knowledge sharing.

Requirements

• Bachelor’s degree in a technical field and 8+ years of professional experience, or a master’s degree and 6+ years, or a doctoral degree and 4+ years.
• Experience developing and delivering production software that includes machine learning models.
• Experience designing and operating production ML systems at scale, including experimentation, deployment, and monitoring.
• Strong SQL skills and experience working with analytical and transactional databases to curate high-quality datasets.
• Ability to break down ambiguous problems into tractable solutions and clear iterations.
• Availability to work four days per week from the NYC office is required.

Nice to have

• Experience developing agentic AI workflows.

Culture & Benefits

• Comprehensive benefits are available to all full-time, exempt employees.
• Potential total compensation may include stock options and other company-sponsored benefits.
• Values include connection, curiosity, evidence-based thinking, urgency, and persistence.
• Mentoring, documentation, presentations, and knowledge sharing support a culture of learning and collaboration.