ML/AI Engineer, Applied AI

Recruiting From Scratch•San Francisco, NY

3d•Onsite

About The Position

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.

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 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 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 Docker and production deployment
Experience with APIs and backend services
Strong software architecture and systems thinking
Strong debugging and production troubleshooting capabilities
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
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

Nice To Haves

Experience with enterprise or complex datasets preferred
Experience with Redis or comparable caching/data systems preferred
Experience with cloud infrastructure such as GCP preferred
Experience with RAG systems preferred
Experience with vector search, hybrid search, chunking, ranking, reranking, or context assembly preferred

Responsibilities

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

Benefits

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

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