Member of Technical Staff

About Eragon AI

Eragon is building an enterprise-grade AI operating system. The thesis is simple: software as we know it is dead. Buttons, menus, and dialog boxes are relics. The future of enterprise runs on prompt-driven, agent-powered tools that companies own, control, and deploy in their own cloud environment.

Eragon post-trains open-source models on customer data, integrates with the entire enterprise stack, including email, Slack, ERPs, and CRMs, and lets employees and executives take action via natural language. When a CEO wants to know which deals might slip, they ask Eragon. When someone needs to onboard a customer, spin up a dashboard, or approve an invoice, Eragon handles it. Company data never leaves its own servers, and model weights become valuable corporate assets over time.

$12M seed raised at a $100M post-money valuation, led by Long Journey Ventures, Soma Capital, and Axiom Partners. $3.5M ARR generated in Q1 alone. 50+ customers deploying both locally and in the cloud. Preemptive Series A interest already incoming. The technical team includes a Berkeley CS PhD and an MIT PhD. Featured in TechCrunch. Josh Sirota, Founder and CEO, is targeting $1B by the end of the year.

About the Role

Eragon is looking for Members of Technical Staff who can handle everything from modeling to systems and product, taking ideas from concept to real-world production without a roadmap handed to them. You will report directly to Josh and work alongside one of the most talented and intense small teams in San Francisco.

The work is some of the most challenging and interesting in AI right now. The culture is beyond 996. Josh lives above the office. If that excites you rather than concerns you, keep reading.

Key Responsibilities

System Development and Deployment: Build, integrate, and deploy AI-powered systems into production environments across enterprise customers

Model Development: Fine-tune, evaluate, and work with machine learning models in real-world applications

Systems Engineering: Design scalable pipelines for training, inference, and data processing

Performance Optimization: Improve latency, throughput, cost efficiency, and reliability of production AI systems

Data and Infrastructure: Work with large-scale datasets and integrate systems with internal tools and APIs across customer stacks

Cross-Functional Collaboration: Partner with product, research, and design to ship end-to-end features

Evaluation and Monitoring: Implement evaluation frameworks, observability, and feedback loops for production AI systems

Minimum Qualifications

Education: Bachelor's or Master's in Computer Science, Engineering, or a related field

Technical Skills: Strong proficiency in Python and modern engineering or ML frameworks

Production Experience: Experience building and deploying systems in production environments

Systems Knowledge: Familiarity with data pipelines, APIs, and cloud infrastructure on AWS or GCP

Practical ML Experience: Experience working with machine learning models or data-driven systems

Startup Mindset: Prior experience in a venture-backed, fast-paced environment, ideally as a founding team member or early employee. Big tech backgrounds typically do not thrive here

Nice to Have

Experience deploying or scaling ML systems in production at a meaningful scale

Familiarity with LLMs, agents, or workflow automation systems

Experience with distributed systems or large-scale infrastructure

Background at a frontier AI lab: Anthropic, OpenAI, DeepMind, or equivalent

High-growth startup background: Databricks, Stripe, Ramp, or equivalent, with a compelling reason for the AI pivot

Top school pedigree: MIT, Stanford, Berkeley, CMU, Waterloo, or equivalent

Has lived and worked in the SF Bay Area or a comparable major startup ecosystem

Prior founding experience or has built and owned something completely end-to-end

Who Will Thrive Here

Someone obsessed with what is happening at the frontier of AI right now. Can code alone for hours, then switch to collaborating on product and strategy without skipping a beat. Has the hunger of someone who could have been a founder. The candidates who have not worked out here either were not ready for the intensity or realized mid-process that they wanted to start their own company. Both are signals of the bar Eragon is setting.