Eragon
Machine Learning Engineer
San Francisco
Sponsorship not specifiedDetected 119 days ago
PythonDistributed SystemsAWSGCPCloud PlatformsMachine LearningTensorFlowPyTorchData EngineeringSystems EngineeringResearchCollaboration
About the role
- 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.
Responsibilities
- Systems Engineering: Design scalable pipelines for training, inference, evaluation, and monitoring
- 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
- Design scalable pipelines for training, inference, evaluation, and monitoring
- Partner with product and engineering teams to deliver end-to-end AI features
Requirements
- Technical Skills: Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX)
- Bachelor's or Master's in Computer Science, Engineering, or related field (PhD optional, not required)
- Familiarity with distributed systems, data pipelines, and cloud infrastructure (e.g., AWS, GCP)
- Experience with model training, fine-tuning, evaluation, and iteration at scale
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
- Implement robust evaluation frameworks, observability, and feedback loops
Benefits
- Model Development & Deployment: Build, fine-tune, and deploy machine learning models into production environments
- Education: Bachelor's or Master's in Computer Science, Engineering, or related field (PhD optional, not required)
- We're looking for a Machine Learning Engineer to build and deploy production-grade AI systems.
This listing is sourced directly from Eragon's careers page and normalized into a canonical job model.