Principal Machine Learning Engineer

Responsibilities

• Lead the technical direction of GenAI and agentic ML systems powering enterprise-grade AI agents, including reasoning, retrieval, tool use, and SaaS integrations.
• Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval-augmented generation (RAG), agent orchestration, and evaluation.
• Define and own the multi-year ML roadmap for GenAI infrastructure, including agent frameworks, RAG systems, evaluation loops, and MCP, browser, and vision integrations.
• Identify, research, prototype, and integrate advances in deep learning, large models, recommender systems, LLMs, reasoning, memory architectures, multimodal perception, long-context models, and autonomous agents.
• Optimize accuracy, latency, cost, interpretability, and reliability across the agent lifecycle, from prompt design through orchestration and execution.
• Drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic systems.
• Mentor senior engineers and researchers while fostering scientific rigor, experimentation, and system-level thinking.
• Collaborate with product, infrastructure, and research teams to align ML innovation with enterprise needs, secure integrations, privacy-aware deployments, and scalable use cases.
• Guide data strategy, including retrieval indices, embeddings, structured and unstructured corpora, and feedback loops.
• Ensure ML agents and RAG pipelines scale across billions of knowledge objects, diverse APIs, and real-time enterprise contexts.

Requirements

• Bachelor's, master's, or PhD in Computer Science, Machine Learning, Statistics, or a related field.
• 10–12+ years of applied machine learning experience, especially in large-scale settings.
• Experience building production ML systems under latency, throughput, and cost constraints.
• Experience with knowledge retrieval and search.
• Exposure to agentic systems and frameworks.
• Proficiency in Python, C++, or Java and ML frameworks such as TensorFlow and PyTorch.
• Strong understanding of the full ML lifecycle, including data pipelines, feature engineering, model training, serving, monitoring, and maintenance.
• Experience designing monitoring, diagnostics, logging, and model-versioning systems.
• Deep knowledge of distributed training and inference optimization techniques such as quantization, pruning, and batching.
• Excellent communication skills and ability to explain complex systems and trade-offs to technical and non-technical stakeholders.
• Experience mentoring senior engineers and leading technical discussions across organizations.

Compensation & Benefits

• Compensation is determined by location, level, job-related knowledge, skills, and experience.
• Certain roles may be eligible for variable compensation, equity, and benefits.