Back to search
Data & AnalyticsOn-site

Machine Learning Engineer

Eragon · On-site

Apply
Last checked by MeritLog September 25, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from Eragon's Ashby job board and last checked it on September 25, 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

Other jobs from this company

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%.

These counts use the job boards we track and were checked within the last five minutes. We compare only full pay ranges in the same currency and time period.

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

Keep exploring

More Data & Analytics roles

Search all jobs

Privacy choices

Analytics and advertising stay off unless you allow them. Private data stays out.

Read the privacy notice