MLAI Engineer, Applied AI

Our client is building a platform that helps the worlds largest organizations capture and operationalize the process knowledge generated through their day-to-day business operations.

The platform transforms complex enterprise operational data into structured living specifications that can be used by businesses and increasingly by agentic AI systems.

The company is operating at the intersection of process intelligence and agentic AI helping enterprises make previously inaccessible operational knowledge available to modern AI systems and workflows.

The founding team has deep experience building process discovery knowledge capture and browser-use AI agents for Fortune 500 enterprises. The company is backed by leading technology investors and operates as a small highly technical early-stage team.

As an ML/AI Engineer Applied AI youll own the AI systems layer responsible for turning messy enterprise data into reliable measurable and useful process intelligence.

This is an opportunity to join a high-ownership AI team where youll work directly across LLM applications retrieval evaluation orchestration structured extraction observability and production AI infrastructure.

• Build and own production AI systems powering enterprise process intelligence
• Design systems that transform messy enterprise data into structured and actionable outputs
• Build retrieval and context-construction pipelines for production AI workflows
• Develop systems for structured extraction classification summarization and other AI-powered workflows
• Build and maintain LLM-powered applications using hosted models from OpenAI Anthropic Gemini Cohere and similar providers
• Design evaluation systems that measure AI quality reliability and regressions
• Build datasets golden sets tests and quality gates for AI systems
• Develop evaluation workflows including offline and online evaluation
• Build LLM-as-judge and human review workflows to measure model performance
• Design and improve multi-step AI workflows tool-calling systems and agent orchestration
• Optimize prompts schemas context selection and model/provider selection
• Build systems that balance model quality latency reliability and cost
• Develop observability and monitoring for production AI systems
• Design retry fallback verification and failure-handling mechanisms
• Improve AI system reliability and debuggability in production
• Work closely with backend product and forward-deployed engineering teams
• Partner directly with engineering teams to solve real customer workflow problems
• Translate ambiguous product goals into experiments implementation measurement and shipped improvements
• Build reliable Python services data pipelines evaluation workflows and AI tooling
• Contribute to architecture and technical strategy across the AI systems layer
• Work with enterprise data sources including documents and other complex data formats
• Improve the quality and usefulness of AI outputs through continuous experimentation
• Operate with high ownership in a fast-moving AI startup environment

• 46 years of professional experience building production software ML systems or applied AI systems
• Strong experience building and shipping production AI systems
• Experience working with hosted LLM APIs in production
• Experience building reliable AI-powered applications and workflows
• Experience designing evaluation and measurement systems for AI outputs
• Experience working with enterprise or complex datasets preferred
• Experience building data pipelines or AI infrastructure
• Experience operating production systems with real users
• Experience collaborating closely with product and engineering teams
• Comfortable translating ambiguous product problems into technical solutions
• Strong ownership mentality with demonstrated execution ability
• Comfortable working in fast-moving and highly ambiguous environments
• Strong understanding of AI system reliability and quality
• Experience iterating AI systems based on measured results and customer feedback

• Strong Python engineering experience
• Strong experience building production services and AI tooling in Python
• Practical experience with hosted LLM APIs such as OpenAI Anthropic Gemini or Cohere
• Experience with prompting and structured outputs
• Experience with embeddings and retrieval systems
• Experience building RAG systems preferred
• Experience with vector search hybrid search chunking ranking reranking or context assembly preferred
• Experience building LLM evaluation systems
• Experience with golden datasets regression testing LLM-as-judge or human review workflows
• Experience with agent or tool orchestration
• Experience with structured extraction and AI workflow pipelines
• Strong understanding of observability retries failure modes and production reliability
• Experience balancing AI quality latency reliability and cost
• Experience with PostgreSQL or relational databases
• Experience with Redis or comparable caching/data systems preferred
• Experience with cloud infrastructure such as GCP preferred
• Experience with Docker and production deployment
• Experience with APIs and backend services
• Strong software architecture and systems thinking
• Strong debugging and production troubleshooting capabilities

• Bachelors degree or higher in Computer Science Engineering Mathematics or related technical field preferred
• Strong computer science machine learning and software engineering fundamentals
• Equivalent practical engineering experience accepted

• Exceptional technical ownership
• Strong analytical and problem-solving ability
• Deep curiosity around AI systems
• Strong evaluation and measurement mindset
• Comfortable working with ambiguity
• Strong communication skills
• Comfortable collaborating across engineering product and customer-facing teams
• High execution velocity
• Strong attention to production reliability and system quality
• Bias toward experimentation and continuous improvement
• Strong ability to reason from first principles
• Comfortable balancing probabilistic AI behavior with deterministic engineering systems
• Low-ego collaborative mentality
• Customer-focused mindset
• Strong builder mentality
• Comfortable working in a small high-performing startup team
• Willingness to work in-office in San Francisco or New York City

• Base Salary: $180000 $275000
• Competitive Equity Package
• Opportunity to build production AI systems for enterprise customers
• Significant ownership over the AI systems layer
• Direct collaboration with founders and engineering leadership
• Opportunity to work across LLMs retrieval evaluation orchestration and AI observability
• Exposure to cutting-edge agentic AI and process intelligence systems
• High-growth early-stage AI environment
• Opportunity to influence AI architecture and product direction
• Opportunity to build systems used by large enterprise organizations

This is an opportunity to join an early-stage AI company building a new category at the intersection of process intelligence and agentic AI.

Youll work directly on the systems that turn complex enterprise data into reliable AI-powered process intelligence while solving some of the hardest problems around LLM evaluation retrieval orchestration quality and production reliability.

As an early engineering contributor youll have significant ownership over architecture and technical direction while working closely with founders product teams backend engineers and customer-facing engineers.

If you enjoy building production AI systems measuring and improving model behavior solving complex enterprise data problems and operating with high ownership in a fast-moving AI environment this role offers exceptional technical and product impact.