Machine Learning Engineer, Level 4 (AI)

Location: United States — Bellevue, Los Angeles, New York, Palo Alto, or San Francisco. Expected to work in an office 4+ days per week under hirify.global's default-together policy.

Base salary: $173,000–$259,000 annually in Zone A, $164,000–$246,000 annually in Zone B, or $147,000–$220,000 annually in Zone C, plus RSU eligibility.

Company

hirify.global is a technology company operating hirify.globalchat and other digital services, building camera-based and visual communication products used by hundreds of millions of people.

What you will do

• Build and deploy machine learning models that power core products at large scale.
• Apply modern machine learning techniques to ranking, recommendations, search, content understanding, image generation, and related problems.
• Own the full machine learning lifecycle from data analysis through production deployment.
• Partner with cross-functional teams to prototype and launch machine-learning-driven features.
• Design and ship scalable services using AI-assisted engineering workflows while maintaining code correctness, security, and production quality.

Requirements

• Bachelor's degree in computer science or a related technical field, or equivalent practical experience.
• At least 3 years of post-bachelor's machine learning experience, or equivalent experience with a relevant master's degree or PhD.
• Strong understanding of machine learning approaches and algorithms.
• Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or similar applications.
• Ability to solve open-ended problems, work independently, collaborate with internal and external partners, and mentor others.
• Proficiency in using AI tools and the judgment to audit generated output for architecture, performance, and security risks.

Nice to have

• Advanced degree in computer science or a related field.
• Experience with TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks.
• Experience with machine learning infrastructure, ranking infrastructure, and system design.

Culture & Benefits

• In-person collaboration through the default-together work approach.
• Paid parental leave and comprehensive medical coverage.
• Emotional and mental health support programs.
• Compensation includes potential long-term participation through RSUs.
• Commitment to an inclusive workplace and equal employment opportunity.