Machine Learning Engineer (Post-Training)
Tasks
- Build data pipelines for training and human feedback collection
- Create evaluation frameworks for agent performance
- Debug training pipelines and model behavior
- Design post training environments for supply chain decision making
- Document training results and failure modes
- Monitor industry trends and apply cutting edge use cases
- Optimize training infrastructure for throughput efficiency fault tolerance
- Translate research ideas into reliable systems
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Data Pipelines | Data-parallel | DeepSpeed | Direct Preference Optimization | Distributed Data Parallel | Distributed Training | Distributed data | Evaluation Frameworks | Fault-tolerant | Fault-tolerant systems | Fine Tuning | Fully Sharded Data Parallel | GCP | Human-in-the-loop | Kubernetes | LLM | Nondeterministic Data Parallel | Policy Optimization | Preference optimization | Proximal Policy Optimization | PyTorch | Python | Reinforcement Learning | Reward Modeling | Reward shaping | Supervised Fine Tuning | The Loop | Transformers
Education
N/A
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