Director, ML Engineering
Tasks
- Build observability for quality latency cost and utilization
- Co design scalable real time serving for on set and high volume generation
- Define self serve model onboarding roadmap
- Define technical strategy for training and inference at scale
- Drive production reliability and SLAs
- Establish engineering operating rhythm and exec reviews
- Lead engineering organization hiring and talent strategy
- Lead productionizing and deployment of custom generative models
- Manage tenancy and data isolation architecture
- Optimize GPU utilization and inference unit economics
- Own engineering operating model and architecture patterns
- Translate customer requirements into ML roadmaps and success metrics
Perks/Benefits
- N/A
Skills/Tech-stack
CUDA | Data pipeline | Diffusion Models | Evaluation Frameworks | Generative AI | Inference Optimization | Lifecycle Management | Machine Learning | Model Lifecycle | Model Lifecycle Management | Model Optimization | Monitoring | NCCL | Observability | Quantization | SLAs | Tenancy Isolation | Transformers | Variational Autoencoders
Education
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