Key Responsibilities • Design, develop, and deploy scalable machine learning models and systems. • Adapt standard ML methods to modern parallel environments (e.g., distributed clusters, multicore SMP, GPU). • Build high-performance classifiers and tools using ML, data regression, and rule-based models. • Collaborate cross-functionally to gather and synthesize requirements. • Own the full ML lifecycle—from ideation to production impact. • Write clean, efficient, and scalable code in collaboration with engineering teams. • Monitor, evaluate, and improve model performance in production environments. Minimum Qualifications • 6+ years of experience in software engineering or a related field (3+ years with a PhD). • 2+ years of hands-on experience in ML, recommendation systems, pattern recognition, or AI. • Proficiency in Python, JavaScript, or Hack. • Proven experience developing ML models at scale and deploying them into production. • Strong track record of cross-functional collaboration and delivering high-quality, reliable systems. • Bachelor's degree in Computer Science, Computer Engineering, or a related technical field. Preferred Qualifications • Master’s or PhD in Computer Science, Data Science, or a related ML field. • Experience with PyTorch, TensorFlow, and C++. • Familiarity with architectural patterns of large-scale software systems. • Background in GenAI, LLMs, and Retrieval-Augmented Generation (RAG). • Experience with MLOps tools (MLflow, Docker, Kubernetes, Prometheus, Grafana). • Exposure to cloud platforms (AWS, Azure) and vector databases (Pinecone, Qdrant). Must-Have Skills • 5+ years of consistent industry-level experience in ML, AI, or GenAI. • Strong coding skills in Python and experience with ML frameworks. • Demonstrated ability to build and scale ML models from inception to production. • Experience with model monitoring, testing, and continuous improvement. • Deep understanding of data structures, algorithms, and mathematical modeling. Nice-to-Have Skills • Experience at major tech companies. • Familiarity with LLMs like GPT, LLaMa, Mistral, and frameworks like LangChain. • Experience with CUDA, GPU acceleration, and real-time inference systems. Interview Process 1. Round 1: 45-minute coding interview • One easy-level algorithm problem • One medium-level algorithm problem 1. Round 2: 45-minute ML system design interview