Member of Technical Staff
Eragon · On-site
MeritLog read this listing from Eragon's Ashby job board and last checked it on September 12, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- $180K - $230K
- Location
- San Francisco
Hiring context
How this role compares at Eragon
Eragon has 4 live roles in MeritLog’s catalog across 2 job families, and 3 of them are in data & analytics. 4 of those listings publish a pay range, a disclosure rate of 100%.
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- 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
What they're asking for
- Education: Bachelor’s or Master’s in Computer Science, Engineering, or related fieldEducation
- Technical Skills: Strong proficiency in Python and modern engineering or ML frameworksSkill
- Production Experience: Experience building and deploying systems in production environmentsSkill
- Systems Knowledge: Familiarity with data pipelines, APIs, and cloud infrastructure (AWS, GCP)Skill
- Practical ML Experience: Experience working with machine learning models or data-driven systemsSkill
- Experience deploying or scaling ML systems in productionSkillPreferred
- Familiarity with LLMs, agents, or workflow automation systemsSkillPreferred
- Experience with distributed systems or large-scale infrastructureSkillPreferred
- Background in fast-paced or early-stage environmentsSkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
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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