[2026] Senior Machine Learning Engineer (Systems), Embodied AI/NPCs, ML Platform - PhD Early Career
Tasks
- Analyze ML performance bottlenecks
- Architect distributed inference systems
- Build AI platform serving layer
- Build pipeline orchestrator
- Deploy models with inference pipelines
- Develop scale data pipelines
- Develop training and inference control planes
- Maintain model registry
- Manage model inference and deployment with MLOps
- Optimize ML models using continuous batching
- Optimize ML models using quantization
- Optimize inference engine latency
- Optimize inference using speculative decoding
- Optimize real-time inference
- Train novel deep learning architectures
Perks/Benefits
Skills/Tech-stack
AWS | Azure | Cloud platform | Continuous batching | Data Pipelines | Deep learning | Distributed Systems | Distributed inference | GPU Architecture | Google Cloud | Google Cloud Platform | High Availability | Kubernetes | Language Models | Large Language Models | Low Latency | Machine Learning | Model Registry | Model Serving | Model Training | Performance Profiling | Pipeline Orchestration | Quantization | Real Time | Real-time Inference | Recommender Systems | Speculative decoding
Education
Regions
Countries
States
Cities
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