Machine Learning Research Engineer (Foundational Research)
Tasks
- Build LLM evaluation frameworks
- Build ML data pipelines
- Build agentic workflow infrastructure
- Collaborate with researchers and translate papers into implementations
- Conduct experiments and iterate on research ideas
- Create automated benchmarking and testing systems
- Design and implement large language model training systems
- Develop instruction fine tuning workflows
- Develop preference optimization workflows
- Develop reinforcement learning workflows
- Generate synthetic data pipelines
- Perform code reviews and share technical best practices
Perks/Benefits
- Flexible work arrangement
- Hybrid work model
- Learning opportunities
- On-the-job coaching
- Work-life balance
Skills/Tech-stack
CI/CD | ClearML | Containerization | Continuous integration | Curriculum learning | Dask | Data Processing | Direct Preference Optimization | Distributed Training | Efficient Attention | Experiment tracking | Fine Tuning | Gradient Checkpointing | HuggingFace Transformers | Instruction Fine-Tuning | JAX | LLM APIs | Large Scale Data | Large-scale | Large-scale Data Processing | MLOps | MLflow | Mixed Precision | Mixed-precision training | Monitoring | Policy Optimization | Preference optimization | Proximal Policy Optimization | PyTorch | Python | Ray | Reinforcement Learning | Spark | Synthetic data | Test automation | Unit Testing | VLLM | Version control | Weights and Biases
Education
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