Machine Learning Engineer
Eragon · On-site
MeritLog read this listing from Eragon's Ashby job board and last checked it on September 10, 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
- Occupation
- Computer Systems Engineers/Architects(O*NET 15-1299.08)
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
- Model Development & Deployment: Build, fine-tune, and deploy machine learning models into production environments
- Systems Engineering: Design scalable pipelines for training, inference, evaluation, and monitoring
- Performance Optimization: Improve latency, throughput, cost efficiency, and reliability of ML systems
- Data & Infrastructure: Work with large-scale datasets and integrate models with internal systems and APIs
- Cross-Functional Collaboration: Partner with product and engineering teams to deliver end-to-end AI features
- Evaluation & Monitoring: Implement robust evaluation frameworks, observability, and feedback loops
What they're asking for
- Education: Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD optional, not required)EducationPreferred
- Technical Skills: Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX)Skill
- Production Experience: Experience deploying and maintaining ML systems in production environmentsSkill
- Systems Knowledge: Familiarity with distributed systems, data pipelines, and cloud infrastructure (e.g., AWS, GCP)Skill
- Practical ML Expertise: Experience with model training, fine-tuning, evaluation, and iteration at scaleSkill
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
JOB DESCRIPTION We’re looking for a Machine Learning Engineer to build and deploy production-grade AI systems. In this role, you’ll take models from research to real-world applications, designing, optimizing, and scaling systems that power critical workflows across the enterprise. You’ll work closely with research, product, and engineering teams to turn cutting-edge capabilities into reliable, high-performance systems in production. KEY RESPONSIBILITIES - Model Development & Deployment: Build, fine-tune, and deploy machine learning models into production environments - Systems Engineering: Design scalable pipelines for training, inference, evaluation, and monitoring - Performance Optimization: Improve latency, throughput, cost efficiency, and reliability of ML systems - Data & Infrastructure: Work with large-scale datasets and integrate models with internal systems and APIs - Cross-Functional Collaboration: Partner with product and engineering teams to deliver end-to-end AI features - Evaluation & Monitoring: Implement robust evaluation frameworks, observability, and feedback loops MINIMUM QUALIFICATIONS - Education: Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD optional, not required) - Technical Skills: Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) - Production Experience: Experience deploying and maintaining ML systems in production environments - Systems Knowledge: Familiarity with distributed systems, data pipelines, and cloud infrastructure (e.g., AWS, GCP) - Practical ML Expertise: Experience with model training, fine-tuning, evaluation, and iteration at scale
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