Staff Machine Learning Engineer

Role overview

Technical leadership and machine learning

• Define the technical vision for ML and GenAI, lead architecture reviews, and establish scalable AI engineering practices.
• Mentor ML engineers and data scientists and influence product strategy through AI innovation.
• Build secure, scalable, and reliable AI platforms with engineering leadership.
• Develop production ML solutions, predictive models, recommendations, forecasts, NLP applications, and deep learning systems.
• Drive the ML lifecycle from problem definition and data exploration through deployment, monitoring, governance, and continuous improvement.
• Create reusable frameworks and establish governance, explainability, fairness, and compliance standards.

Generative AI and platform engineering

• Deliver enterprise-scale solutions using models listed including OpenAI, Azure OpenAI, Anthropic Claude, Llama, Mistral, and Gemini.
• Design RAG, agentic and multi-agent systems, AI orchestration, prompt engineering and evaluation, fine-tuning, and knowledge graph-assisted AI.
• Develop AI observability and evaluation frameworks and lead adoption of emerging AI technologies.
• Architect ML platforms and end-to-end pipelines; advance CI/CD, model serving, feature stores, experiment tracking, monitoring, retraining, governance, and security.
• Optimize system performance, scalability, reliability, and cost efficiency.

Collaboration and business outcomes

• Partner with product managers, engineering leaders, and business stakeholders to identify high-impact AI opportunities.
• Translate business problems into scalable AI solutions, define success metrics, measure impact, and drive organizational AI adoption.

Preferred qualifications

• Preferred experience includes 10+ years in machine learning, data science, and AI engineering, production AI/ML delivery at scale, technical initiative leadership, and mentoring.
• Preferred technical skills include Python, SQL, Spark or other distributed computing, and PyTorch, TensorFlow, or scikit-learn.
• Preferred expertise includes LLMs, RAG, agentic AI, reinforcement learning concepts, and AI evaluation.
• Preferred hands-on tools include Docker, Kubernetes, AWS, Azure, or GCP, vector databases, and API or microservices architecture.
• Preferred MLOps expertise includes deployment, feature stores, experiment tracking, observability, and monitoring.
• The preferred leadership profile includes systems thinking, communication, stakeholder management, cross-functional influence, mentoring, innovation, and continuous learning.

Generative AI technologies

• The posting lists OpenAI, Azure OpenAI, Anthropic Claude, Llama, Mistral, and Gemini as GenAI model options.