Member of Technical Staff
Eragon · On-site
MeritLog read this listing from Eragon's Ashby job board and last checked it on September 26, 2026.
Source: the employer's Ashby job board. Open the job post for the latest details.
Job details
- Work model
- On-site
- Salary
- Not listed by source
- Location
- San Francisco
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How this role compares at Eragon
Eragon has 4 live roles in MeritLog’s job list across 2 job types, and 3 of them are in data & analytics. 0 of those jobs list a pay range. That is 0%.
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Job description
JOB DESCRIPTION We’re looking for a Member of Technical Staff to build and deploy production-grade AI systems. In this role, you’ll work across modeling, systems, and product to take ideas from concept to real-world applications. KEY RESPONSIBILITIES - System Development & Deployment: Build, integrate, and deploy AI-powered systems into production environments - 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 systems - Data & Infrastructure: Work with large-scale datasets and integrate systems with internal tools and APIs - Cross-Functional Collaboration: Partner with product, research, and design to ship end-to-end features - Evaluation & Monitoring: Implement evaluation frameworks, observability, and feedback loops MINIMUM QUALIFICATIONS - Education: Bachelor’s or Master’s in Computer Science, Engineering, or 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 (AWS, GCP) - Practical ML Experience: Experience working with machine learning models or data-driven systems NICE TO HAVE - Experience deploying or scaling ML systems in production - Familiarity with LLMs, agents, or workflow automation systems - Experience with distributed systems or large-scale infrastructure - Background in fast-paced or early-stage environments
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