Member of Technical Staff, Forward Deployed AI Engineer (AI)

Location: Bay Area, in office; occasional travel for strategic deployments, workshops, and executive technical sessions.

Annual base salary: $175,000–$275,000 USD, plus equity and benefits.

Company

hirify.global develops diffusion-based large language models, including Mercury, for faster and more efficient enterprise AI applications.

What you will do

• Work directly with enterprise customers to identify high-value AI workflows and turn rapid proof-of-concepts into production deployments.
• Build full-stack AI applications, agentic workflows, APIs, integrations, internal tools, and customer-facing systems.
• Collect and structure customer data, define deployment metrics, and build LLM-as-judge evaluation systems and feedback loops.
• Tune Mercury models, prompts, workflows, and system architecture for customer-specific performance goals.
• Optimize latency-sensitive agentic applications involving search, coding agents, voice, routing, classification, and context compaction.
• Convert deployment learnings into reusable product patterns, evaluation frameworks, implementation playbooks, and platform capabilities.

Requirements

• BS, MS, or PhD in Computer Science, Machine Learning, or a related field, or equivalent experience.
• Strong software engineering fundamentals, including data structures and algorithms; the role involves spending 70% or more of the time writing code.
• Strong Python and modern full-stack development skills, including APIs and backend systems; TypeScript or JavaScript is preferred.
• Experience building, deploying, or integrating AI or LLM products with real users or customers.
• Experience with LLM evaluation, LLM-as-judge workflows, data pipelines, model tuning, prompt optimization, or agentic workflows.
• Ability to work in person with customers in the Bay Area and travel occasionally for strategic deployments.

Nice to have

• Experience with RAG, search, voice AI, coding agents, or agentic workflow systems.
• Experience deploying AI systems for Fortune 500 or large enterprise customers.
• Familiarity with LLM serving, latency optimization, production ML systems, synthetic data, or model feedback loops.
• Background in product engineering, applied AI, ML product engineering, or forward deployed engineering.
• Experience with customer-specific evaluations, benchmarks, and performance targets.

Culture & Benefits

• Early-stage startup environment with rapid iteration, ambiguity, customer-facing work, and end-to-end ownership.
• Collaboration with AI researchers and engineers who developed diffusion models and other foundational technologies.
• Competitive salary, equity, flexible vacation, and paid time off.
• Health, dental, and vision insurance, plus a 401k match.
• Catered meals and commuter subsidies.