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Job description
About the role
The role addresses operating knowledge within industrial environments, translating procedures into actions for AI agents. It serves leaders in manufacturing, field services, and utilities by orchestrating work contexts. You will own the end-to-end process of converting static documentation and live sensor data into coherent, executable workflows for industrial AI. This position requires you to act as the bridge between tribal expertise and machine-readable instructions that guide autonomous systems. You will design the structure of context so that Fortune 500 operations can execute consistently and remain auditable. Success in this role means ensuring that every procedure captured in the knowledge graph translates reliably into agent behavior on the shop floor. You will work closely with operations teams to validate that orchestrated actions reflect real-world constraints and safety requirements. The role demands comfort with ambiguity as you define the boundaries of what can be automated in complex industrial settings.
Key facts
Operating knowledge is converted into executable procedures that guide industrial workflows. Context for AI agents is established so that essential documents and sensor data inform timely decisions. Work is orchestrated across teams so that standard operating procedures align with live field execution. Actions are taken to ensure Fortune 500 leaders can rely on consistent, auditable outcomes from AI agents.
Requirements
A degree is required as stated in the degree requirement field. You must be able to work in San Francisco. You must be comfortable translating dense procedural documents into structured data that AI agents can consume. You must ensure procedures are precise enough to support orchestrated work for industrial AI deployments. You should possess strong analytical reasoning to map dependencies between documents, systems, and physical actions. You must communicate clearly with both technical and non-technical stakeholders to align on requirements and constraints. You need to be meticulous about data quality and traceability, knowing how each piece of information feeds into agent logic. You must be eligible to work without sponsorship in the San Francisco location.
Nice to have
Relevant experience with industrial AI, manufacturing, field services, or utilities is valued. Background in capturing tribal knowledge and standards documentation is preferred. Experience with orchestration platforms and OT data contexts is considered an advantage.
Practical notes
The role is based in San Francisco with company-wide retreats and potential commute support. You will engage with enterprise customers in manufacturing, field services, and utilities. Typical interview steps include data interviews that commonly feature a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies provide a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
About the company
Squint is the Industrial Intelligence Platform, built for the workers who build the things you touch and see every single day. We're the only solution that brings together all the context of an industrial organization into a custom industrial knowledge graph, unique to every customer.
What you'll do
You will translate standard operating procedures and tribal expertise into structured, machine-readable instructions that power industrial AI agents. This involves capturing, organizing, and orchestrating knowledge so it can directly inform decision-making and action in live operations. You will work with manufacturing, field services, and utilities teams to model workflows and ensure procedures align with physical execution. You will define the context and constraints that AI agents use to perform tasks reliably and safely. This includes designing data structures, process mappings, and rule sets that turn documents and sensor feeds into auditable, repeatable actions. You will validate agent behavior against real-world scenarios to confirm that outputs remain consistent with operational intent. You will collaborate with data and engineering teams to integrate knowledge graphs, orchestration platforms, and OT data contexts into a unified execution layer. You will help build metrics and monitoring that track how well procedural knowledge is executed by automated systems. Across projects, you will ensure traceability from documentation to deployed actions, maintaining clarity for both human operators and autonomous agents.
Requirements
A degree is required as stated in the degree requirement field. You must be able to work in San Francisco. You must handle translation of documents and data into agent-ready procedures and ensure these procedures support orchestrated work for industrial AI deployments. You should possess strong analytical reasoning to map dependencies between documents, systems, and physical actions. You must communicate clearly with both technical and non-technical stakeholders to align on requirements and constraints. You need to be meticulous about data quality and traceability, understanding how each piece of information feeds into agent logic. You must be eligible to work without sponsorship in the San Francisco location. Relevant experience with industrial AI, manufacturing, field services, or utilities is valued. Background in capturing tribal knowledge and standards documentation is preferred. Experience with orchestration platforms and OT data contexts is considered an advantage.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.