Lead Machine Learning Engineer, Foundation Models
Tasks
- Design and optimize distributed training pipelines
- Integrate foundation world models into applications
- Lead end to end development of foundational world models
- Make architectural decisions for transformers diffusion models state space models and latent variable models
- Mentor team members on foundation world model development
- Optimize model inference for low latency and high throughput
- Translate research papers into production code
Perks/Benefits
- Birthday leave
- Flexible work arrangements
- Health and wellness benefits
- Medical insurance
- Parental leave
- Term life insurance
- Volunteering leave
Skills/Tech-stack
Data Deduplication | Data Pipelines | Data Quality | Data Quality Filtering | DeepSpeed | Diffusion Models | Distributed Training | FSDP | High Throughput | JAX | Language Models | Large Language Models | Latent variable models | Low Latency | Low-Latency Systems | MLOps | Mamba | PyTorch | Python | Ray | Self-Supervised Learning | Self-supervised | Sequence Modeling | Sequence to sequence | Sequence-to-Sequence Modeling | State Space Models | State-Space | Supervised Learning | Transformers | Vision Language Models | Vision-language
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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