Parable

Compensation

Full Description

Overview We are building a new AI/ML team at Parable, and are looking for a Senior/Staff SWE to join us. This role places you in the eye of the AI storm – guiding the execution of greenfield projects in the rapidly evolving field of AI, shipping AI pipelines that provide deep insight to customers, and augmenting the product vision through ML. You will work alongside our CTO and seasoned engineers on a team that develops AI agents and pipelines to drive insights for clients, and builds software to automate those insights. You bring significant, hands-on experience building tools and workflows with LLMs. You have a deep understanding of the current technologies' capabilities and constraints, and you have a clear vision for where AI is headed in the near future. You want to build rapidly and yet remain close to our end users’ needs. What is Parable Parable’s mission is to make time matter. Parable provides CEOs of companies with 1,000+ employees with deep observability of the time spent by their team across all strategies, projects, and processes. Our insights help teams focus on the work that matters the most, and drive data-driven decisions in resource allocation. The company was founded by seasoned founders, with multiple 9-figure exits under their belts, and reached $2m of ARR within 6 months of going to market. Parable raised $17 million from investors like HOF Capital and Story Ventures, as well as 50+ founders and executives. On the technical side, we are building an operating system for large enterprises – ingesting silo’ed data from across the workplace stack, structuring this data into our enterprise ontology, and contextualizing it to draw sharp insight on our client companies. You’ll be responsible for: Architecting, building, productionalizing, and supporting agentic workflows to power user frontend experiences like on-the-spot custom graphing or on-the-spot custom data introspection. Integrating and extending existing analytics agentic workflows into modern frontends (React, Vue, etc.) to power on-the-spot data introspection within frontend latency and big data constraints. Designing, building, maintaining, and deploying MCP (Model Control Plane) servers for orchestrating shared agentic use cases across varied agentic hosts. Building performant, secure, and scalable backends that manage user context particularly as it relates to data privacy and RBAC. Contributing to evaluation systems for GUI-focused agentic AI applications and developing new agentic solutions against eval systems. Leading productionalization tracing for LLM calls in agentic workflows. This role is for someone who: Thrives at the intersection of experimentation and production. You're not just a researcher or just an engineer – you're both. You can rapidly prototype agentic workflows for and make choices for scale and reliability. You have a track record of delivering results that have direct impact to users. Exercises extreme ownership. You take complete responsibility for your projects, cast no blame, and make no excuses. When you see a problem, you point it out and bring a solution that solves it already made. You're comfortable leading projects end-to-end, from initial concept to production deployment to on-call support. Is obsessed with data and stays connected to the details. You understand that the quality of insights we deliver to our end users is predicated on the quality of your data and your deep understanding of it. You have a natural curiosity that drives you to develop a close relationship with the data down to its raw form. In the first 3 months you’ll: Own scheduled client deliverables, proposing and testing methodologies to transform unstructured workplace data into meaningful insights about time usage. Contribute data transformation flows that structure and transform raw data to intermediate or final data states needed to power client analytics. Prototype and finalize the design of the work categorizer agentic workflow that will be utilized by multiple software products. Prototype agentic workflows which can deliver analytics in an automated way from any data point. Requirements 3–5+ years of fullstack development experience, , with at least 1 year in senior or lead-level responsibilities. 0.5–2 years integrating AI or LLM APIs into production applications. Proficient in modern frontend frameworks (React, Next.js, Vue) and backend stacks (Node.js, Python/FastAPI, Golang, or similar). Strong programming skills in Typescript and/or Golang. Reading competency in Python. Strong understanding of LLM calling at the API level (when to use resources, tools, function calling for which use-cases). Familiarity with authentication, state management, rate limiting, and API error handling for production-grade applications. Comfortable with cloud deployment (e.g. AWS, GCP, or similar) and database integration (PostgreSQL, Redis, etc.). Sufficient understanding of AI guardrail options at production.