Head of Machine Learning
ChatGPT Jobs
•
Seattle, WA
0
0
0
0
Not Applicable
Posted April 17, 2026
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Responsibilities
Commitments
Responsibilities
- ML & GenAI Strategy
- Define 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 Leadership
- Architect and oversee scalable LLM/GenAI systems for MarTech/AdTech use cases, including:
- Content generation and optimization
- Sentiment and resonance analysis
- Strategy recommendation and campaign optimization
- Audience segmentation, targeting, and personalization
- Design 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 Excellence
- Establish best practices for LLMOps / MLOps, including:
- Model evaluation, A/B testing, and continuous learning
- Monitoring, drift detection, and reliability at scale
- Safe and explainable AI practices
- Oversee 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 Building
- Build, 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 Collaboration
- Work 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.
Commitments
Remote
Full-time
Posted 1 hour ago
Position Type: Full-Time, Senior level
Travel Requirement: 0-10%, Quarterly for meetings
Office Location: Remote, US Based
Not Met Priorities
What still needs stronger evidence
Requirements
- Architect and oversee scalable LLM/GenAI systems for MarTech/AdTech use cases, including:
- 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.
- Deep 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).
Preferred Skills
- Deep expertise in Deep Learning and NLP (PyTorch preferred; TensorFlow acceptable).
- Proven experience fine-tuning and deploying foundation models (LLaMA, Mistral, GPT-style models).
- Experience with vector search and retrieval systems (FAISS, PGVector, Pinecone, Redis Vector).
- Strong background in cloud-native ML systems (AWS, GCP, or Azure).
- Solid understanding of distributed systems, GPU optimization, batching, and cost-aware inference.
Education
- (Not required) – PhD degree in Computer Science, Engineering, or related field
Job Description
Head of Machine Learning
Clarvos LLC
Seattle, WA
Remote
Full-time
Posted 1 hour ago
Job Description
Reports to: Sr VP AI Platform, Interim CTO
Department: GenAI Modeling
FLSA Category: Exempt
Position Type: Full-Time, Senior level
Travel Requirement: 0-10%, Quarterly for meetings
Office Location: Remote, US Based
Job Summary
As 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 Responsibilities
ML & GenAI Strategy
Define 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 Leadership
Architect and oversee scalable LLM/GenAI systems for MarTech/AdTech use cases, including:
Content generation and optimization
Sentiment and resonance analysis
Strategy recommendation and campaign optimization
Audience segmentation, targeting, and personalization
Design 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 Excellence
Establish best practices for LLMOps / MLOps, including:
Model evaluation, A/B testing, and continuous learning
Monitoring, drift detection, and reliability at scale
Safe and explainable AI practices
Oversee 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 Building
Build, 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 Collaboration
Work 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 Qualifications
Education & 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 field
Deep 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).
Head of Machine Learning
Clarvos LLC
Seattle, WA
Remote
Full-time
Posted 1 hour ago
Job Description
Reports to: Sr VP AI Platform, Interim CTO
Department: GenAI Modeling
FLSA Category: Exempt
Position Type: Full-Time, Senior level
Travel Requirement: 0-10%, Quarterly for meetings
Office Location: Remote, US Based
Job Summary
As 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 Responsibilities
ML & GenAI Strategy
Define 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 Leadership
Architect and oversee scalable LLM/GenAI systems for MarTech/AdTech use cases, including:
Content generation and optimization
Sentiment and resonance analysis
Strategy recommendation and campaign optimization
Audience segmentation, targeting, and personalization
Design 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 Excellence
Establish best practices for LLMOps / MLOps, including:
Model evaluation, A/B testing, and continuous learning
Monitoring, drift detection, and reliability at scale
Safe and explainable AI practices
Oversee 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 Building
Build, 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 Collaboration
Work 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 Qualifications
Education & 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 field
Deep 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).