Principal Machine Learning Engineer (Memory & Context)
New
J
JobgetherMachine learning
Based in the United States; Fully remote position for eligible U.S.-based employeesFull-TimePrincipal
Salary$209,400–$334,600 annually in California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington State, and Washington, D.C.; $199,000–$317,800 annually in Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia.
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Job Details
- Experience
- At least five years of experience deploying large-scale machine learning systems, including at least one recent year building and shipping LLM-based agents in production.
- Required Skills
- PythonMachine LearningPrompt Engineering
Requirements
- Master's degree or higher in Computer Science, Electrical Engineering, or a related technical discipline, or equivalent practical experience.
- At least five years of experience deploying large-scale machine learning systems.
- At least one recent year building and shipping LLM-based agents in production.
- Hands-on expertise building AI agents, including orchestration, prompt engineering and optimization, and context engineering.
- Experience evaluating agentic systems and using evaluation results to improve quality, reliability, and effectiveness.
- Strong proficiency in Python.
- Familiarity with agentic AI frameworks such as LangGraph, Agents SDK, or AutoGen.
- Track record of influencing technical direction across multiple teams, leading complex initiatives, and mentoring engineers.
- Preferred: experience designing long-term, episodic, or user-profile memory systems for AI agents.
- Preferred: familiarity with dialogue systems supporting extended, multi-session conversations and personalization using conversation history.
- Additional advantages include research publications, patents, or open-source contributions involving agentic AI, memory architectures, or long-context models.
Responsibilities
- Architect systems for storing, retrieving, updating, and retiring user memories across conversations.
- Design strategies to assemble, prioritize, and compress conversation history, memories, tools, and agent capabilities into context.
- Lead end-to-end development of production-grade agentic systems, including orchestration, prompt engineering, context design, deployment, monitoring, and optimization.
- Establish offline and online evaluation methods for memory quality, context relevance, and agent performance.
- Explore emerging agent architectures, memory systems, and long-context techniques, and translate useful research into product capabilities.
- Lead complex engineering initiatives and influence architecture and implementation decisions across teams.
- Partner with engineering, product, and technical stakeholders to align priorities, clarify requirements, resolve dependencies, and deliver solutions.
- Mentor engineers through design reviews, code reviews, and technical guidance.
- Communicate architectural decisions, evaluation findings, project progress, and recommendations to engineering teams and senior leadership.
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