Staff Machine Learning Engineer (AI)

Location: Mountain View, California, United States

Base pay: $230,000–$250,000 per year. A separate Mountain View range of $202,500–$274,000 is also listed.

Company

hirify.global is a global financial technology platform whose products include TurboTax, Credit Karma, QuickBooks, and Mailchimp.

What you will do

• Design and own shared AI/ML components, model training and fine-tuning pipelines, evaluation frameworks, and ML tooling used across product teams.
• Apply prompting, fine-tuning, RAG, and other LLM techniques to customer problems and evaluate model performance in production.
• Build long-term memory and context-retention systems for personalized, coherent AI experiences.
• Prototype agent builder frameworks and embedded AI experiences, taking agentic AI concepts into production-ready implementations.
• Architect and build full-stack AI-native applications, including backend services and production LLM integrations.
• Establish engineering and ML tooling practices, mentor engineers, and collaborate with AI scientists, product managers, designers, and engineering teams.

Requirements

• BS, MS, or PhD in Computer Science, or equivalent practical experience.
• 8+ years of experience building production AI/ML systems; experience leading an engineering effort is beneficial.
• Strong computer science and machine learning fundamentals, including data structures, algorithms, system design, classification, regression, clustering, and neural networks.
• Proficiency in Python, PyTorch, TensorFlow, Pandas, and NumPy.
• Practical LLM experience with prompt engineering, fine-tuning, LangChain, agentic systems, memory architectures, and model or agent evaluation pipelines.
• Experience with AWS and SageMaker, scalable services, microservices, relational or NoSQL data stores, Kubernetes, and JavaScript or Java.

Culture & Benefits

• Work closely with global teams across AI science, product, design, and engineering.
• Compensation may include performance-based cash bonuses, equity rewards, and benefits.
• Compensation is assessed based on job-related knowledge, skills, experience, and work location.