Head of Machine Learning

Job DescriptionHead of Machine LearningClarvos LLCSeattle, WA RemoteFull-timePosted 1 hour agoJob DescriptionReports to: Sr VP AI Platform, Interim CTODepartment: GenAI ModelingFLSA Category: ExemptPosition Type: Full-Time, Senior levelTravel Requirement: 0-10%, Quarterly for meetingsOffice Location: Remote, US BasedJob SummaryAs Head of Machine Learning, you will own and lead the company's end-to-end ML and GenAI strategy. This is a senior technical leadership role with hands-on architectural responsibility, reporting into executive leadership and partnering closely with Product, Engineering, and Data.You will define the ML vision, build and lead the ML organization, and deliver production-grade AI systems that power our core MarTech and AdTech capabilities, from intelligence and reasoning to execution and optimization.This role is ideal for a principal-level Lead or Director who can operate across strategy, architecture, and execution.Essential Functions And ResponsibilitiesML & GenAI StrategyDefine and own the ML and GenAI roadmap, aligned with product, business, and platform strategy.Establish architectural standards for LLMs/SLMs, agentic systems, real-time inference, and feedback-driven learning loops.Drive the transition toward a fully AI-native, agent-powered platform.System Architecture & Technical LeadershipArchitect and oversee scalable LLM/GenAI systems for MarTech/AdTech use cases, including:Content generation and optimizationSentiment and resonance analysisStrategy recommendation and campaign optimizationAudience segmentation, targeting, and personalizationDesign and deploy multi-agent systems using frameworks such as LangGraph, AutoGen, CrewAI, MCP, or equivalent.Own end-to-end ML system design: data ingestion, feature pipelines, training, inference, evaluation, and monitoring.Lead decisions around foundation models, fine-tuning strategies, RAG pipelines, embeddings, and ranking systems.ML Operations & Production ExcellenceEstablish best practices for LLMOps / MLOps, including:Model evaluation, A/B testing, and continuous learningMonitoring, drift detection, and reliability at scaleSafe and explainable AI practicesOversee scalable training and inference infrastructure, including multi-GPU environments (A100/H100-class systems).Ensure ML systems meet performance, cost, and latency requirements for real-time production use.Team Leadership & Org BuildingBuild, mentor, manage, and scale a high-performing ML organization across senior, principal, and junior talent.Set technical bar, review standards, and guardrails to ensure quality and sustainability in an AI-augmented development environment.Partner with Engineering leadership to balance velocity, code quality, and long-term maintainability.Cross-Functional CollaborationWork closely with Product, Data, and Platform teams to translate business needs into scalable ML capabilities.Communicate complex ML concepts clearly to executive leadership, stakeholders, and the Board.Contribute to technical narratives used for fundraising, company valuation, and strategic planning.Knowledge, Skills, Abilities, And QualificationsEducation & Experience:8-12+ years of experience in ML/AI roles, including senior or principal-level ownership of production ML systems.3+ years of hands-on experience building with LLMs / SLMs in real-world applications.PhD degree in Computer Science, Engineering, or related fieldDeep expertise in Deep Learning and NLP (PyTorch preferred; TensorFlow acceptable).Proven experience fine-tuning and deploying foundation models (LLaMA, Mistral, GPT-style models).Strong command of Hugging Face ecosystem and fine-tuning techniques (LoRA, PEFT, adapters).Experience with vector search and retrieval systems (FAISS, PGVector, Pinecone, Redis Vector).Familiarity with agent-based and reasoning frameworks (LangGraph, ReAct, AutoGen, CrewAI, MCP, etc.).Experience with ML experimentation and observability tools (MLflow, Weights & Biases, PromptLayer, etc.).Strong background in cloud-native ML systems (AWS, GCP, or Azure).Solid understanding of distributed systems, GPU optimization, batching, and cost-aware inference.Excellent software engineering fundamentals (Python, APIs, microservices, Docker, Kubernetes).