Eragon
Machine Learning Engineer
San Francisco$180k–$230kfulltimemidAdded 1 month ago
About this role
San Francisco-based role focused on productionizing machine learning systems at enterprise scale. You'll shepherd models from research through deployment, optimizing performance and reliability while collaborating across research, product, and engineering teams.
What you'll do
- Build, fine-tune, and deploy ML models to production environments
- Design scalable pipelines for training, inference, evaluation, and monitoring
- Optimize ML systems for latency, throughput, cost, and reliability
- Manage large-scale datasets and integrate models with internal systems and APIs
- Implement evaluation frameworks and observability for deployed models
- Partner with cross-functional teams to deliver end-to-end AI features
What they're looking for
- Python
- ML frameworks (PyTorch, TensorFlow, JAX)
- Production ML systems deployment
- Distributed systems and data pipelines
- Cloud infrastructure (AWS, GCP)
- Model training and fine-tuning
- Performance optimization
- Monitoring and evaluation frameworks
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Eragon
Eragon builds machine learning infrastructure and systems for enterprise-scale deployment. The company is hiring for roles focused on productionizing ML models, optimizing their performance and reliability across research, product, and engineering functions.
View all jobs at EragonLikely interview questions
- Walk us through a machine learning model you deployed to production. What were the main challenges in moving from research to production, and how did you address them?
- Describe your experience optimizing ML systems for latency and cost. What trade-offs did you consider, and what tools or techniques did you use?