Software Engineer, Machine Learning
Tasks
- Align research infrastructure and product teams
- Assess reliability privacy and integrity risks
- Automate model evaluation pipelines
- Build feature engineering platforms
- Build technical safeguards for responsible AI
- Communicate ML system designs and trade offs
- Create AI native workflows and tooling
- Define ML metrics and data driven decisions
- Define machine learning system architecture
- Design inference infrastructure
- Develop extensible ML frameworks
- Establish engineering practices
- Identify performance bottlenecks across training data loading and serving
- Lead cross functional ML technical strategy
- Mentor engineers on ML systems design and debugging
- Optimize large scale ML system performance
- Own model training pipelines
- Publish technical materials and design frameworks
- Set modeling standards
- Solve complex ML systems challenges
Perks/Benefits
- N/A
Skills/Tech-stack
Agent Orchestration | Automation | Bias Mitigation | Computer Vision | Data Integrity | Data Pipelines | Deep learning | Developer Tooling | Distributed Systems | Distributed Training | Feature Engineering | Fine Tuning | Foundation Models | Generative AI | Inference Serving | Language Processing | Low Latency | Low Latency Inference | Machine Learning | Model Evaluation | Model Training | Model metrics | Model training pipelines | Natural Language | Natural Language Processing | Privacy Risk | Privacy risk assessment | Prompt engineering | Ranking and Recommendation | Responsible AI | Risk Assessment | Training pipelines | Training stability
Education
Regions
Countries
States
Cities
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