Senior Machine Learning Engineer (AI)

TL;DR

Senior Machine Learning Engineer (AI): Designing and building next-generation agentic AI systems to augment contact center agents with an accent on LLM integration, RAG pipelines, and multi-step reasoning architectures. Focus on solving complex failure modes like hallucinations and retrieval errors while ensuring system reliability, low latency, and production scalability.

Location: Must be based in the United States (Remote)

Salary: $205,000–$270,000

Company

hirify.global is an AI-driven platform born from the Stanford AI lab, dedicated to revolutionizing contact center productivity by combining AI and human intelligence to automate processes and provide real-time agent assistance.

What you will do

• Lead the design and development of next-generation AI agents and intelligent workflows for contact centers.
• Architect and optimize LLM-powered systems, including RAG pipelines and domain-adapted models.
• Define and implement evaluation strategies for non-deterministic systems, including offline benchmarking and online experimentation.
• Diagnose and mitigate failure modes such as hallucinations, tool misuse, and context drift.
• Define and track performance metrics including accuracy, faithfulness, latency, and cost efficiency.
• Mentor engineering team members and contribute to the overall technical AI strategy and roadmap.

Requirements

• Bachelor’s degree in Computer Science, Mathematics, or related field (Master’s or Ph.D. preferred).
• 5–8+ years of industry experience in building and deploying ML systems in production.
• Significant experience working with LLMs, transformer architectures, embeddings, and NLP.
• Proven track record of designing and deploying RAG systems in enterprise environments.
• Expertise in modern ML frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Demonstrated ability to optimize real-time ML systems for performance and scalability.

Nice to have

• Experience building and evaluating complex agentic or multi-step LLM workflows.
• Strong background in distributed or cloud-based ML infrastructure.

Culture & Benefits

• Comprehensive medical, dental, and vision coverage.
• Flexible PTO policy.
• Paid parental leave.
• Retirement savings plan.
• Dedicated remote work setup budget.
• Monthly wellness and communication stipend.