Building and deploying state-of-the-art machine learning models to power intelligent experiences for millions of users
Requirements
- 7+ years of hands-on ML experience with production model deployment
- Expert-level proficiency in Python and ML frameworks (PyTorch/TensorFlow/JAX)
- Deep understanding of transformer architectures, attention mechanisms, and modern NLP
- Experience with large-scale distributed training (multi-GPU, model/data parallelism)
- Strong background in statistics, linear algebra, and optimization theory
- Experience with MLOps tools: MLflow, Weights & Biases, Kubeflow, or similar platforms
- Proficiency with cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML)
Responsibilities
- Design and train custom neural networks for human-centered AI using PyTorch/TensorFlow
- Fine-tune and adapt large language models (Llama, Claude, GPT) for domain-specific tasks
- Implement novel architectures from recent papers (attention mechanisms, retrieval augmentation)
- Conduct rigorous A/B testing and evaluation of model performance in production
- Build and maintain scalable ML pipelines processing 10M+ daily inferences
- Implement real-time model serving with <100ms p95 latency requirements
- Design and optimize vector similarity search systems for multi-modal embeddings
Other
- 7+ years of experience
- PhD in ML/AI, Computer Science, or equivalent industry experience (preferred)
- Publications in top-tier conferences (NeurIPS, ICML, ICLR, EMNLP) (preferred)
- Experience at AI-first companies or research labs (OpenAI, Anthropic, DeepMind, etc.) (preferred)
- Contributions to open-source ML projects with significant community adoption (preferred)
