ML/AI Engineer, Applied AI

Location - San Francisco, CA / New York, NY (On-site) - In-office in San Francisco or New York City.

Compensation - $180,000 – $275,000 Base + Competitive Equity

Visa - Open to Visa Transfers (OPT, H1B Transfers)

Company Stage - Early Stage – $5.5M Funding

Industry - Artificial Intelligence, Machine Learning, Enterprise Software, B2B SaaS, Process Intelligence, Agentic AI

About the Company

Our client is building a platform that helps the world's 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, you'll 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 you'll work directly across LLM applications, retrieval, evaluation, orchestration, structured extraction, observability, and production AI infrastructure.

What You'll Do

• 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

Ideal Candidate Background

Experience Requirements

• 4–6+ 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

Technical Requirements

• 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

Education

• Bachelor's 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

Soft Skills

• 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

Compensation & Benefits

• Base Salary: $180,000 – $275,000
• 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

Why Join

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

You'll 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, you'll 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.