Senior Machine Learning Engineer

Key Responsibilities

Training Pipeline & Experimentation

• Own the end-to-end training pipeline, including data ingestion, orchestration, checkpointing, and result logging
• Execute large-scale experiments with strong emphasis on reproducibility and traceability
• Investigate training instabilities, loss anomalies, and performance gaps, providing structured analysis and hypotheses
• Implement and validate new optimization techniques and training objectives in collaboration with senior ML leadership
• Continuously improve pipeline efficiency to reduce iteration time while maintaining experiment quality
• Manage compute resources across parallel experiments, balancing throughput and cost efficiency

Evaluation & Benchmarking

• Design and maintain comprehensive evaluation and benchmarking frameworks
• Define clear success metrics across accuracy, latency, memory usage, and domain coverage
• Build automated evaluation pipelines to detect regressions across model checkpoints
• Analyze results to identify patterns in model performance and quality trade-offs
• Partner with Data teams to ensure improvements in training data translate to measurable gains
• Maintain and evolve benchmarking methodologies aligned with industry best practices

Infrastructure & Collaboration

• Partner with Platform Engineering on distributed training infrastructure and experiment tracking systems
• Develop internal tooling to support model analysis and research workflows
• Contribute to team standards around reproducibility, experiment tracking, and documentation
• Collaborate with Platform teams to support model deployment, optimization, and serving

Qualifications Required

Education & Experience

• MS or PhD in Computer Science, Engineering, Mathematics, or related field
• 5+ years of experience in Machine Learning, Applied AI, or related areas
• Proven experience training and evaluating large-scale language and/or vision-language models
• Strong background in building evaluation frameworks and benchmarking systems
• Experience with model optimization or efficient training techniques

Technical Expertise

• Deep understanding of model optimization and compression (e.g., quantization, pruning)
• Strong proficiency in Python and PyTorch, including distributed training frameworks (e.g., DeepSpeed, FSDP)
• Experience managing large-scale training runs (job scheduling, checkpointing, fault tolerance)
• Expertise in evaluation methodology and benchmark design
• Experience with experiment tracking and reproducibility practices
• Familiarity with vision-language model architectures and document AI challenges