Principal Machine Learning Engineer (Memory & Context)

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Principal Machine Learning Engineer (Memory & Context) based in the United States. As a Principal Machine Learning Engineer, you will shape the memory and context capabilities behind advanced AI assistants, enabling them to deliver increasingly personalized and relevant experiences. You will design systems that help AI agents remember user preferences, understand evolving needs, and maintain continuity across conversations over extended periods. Working at the intersection of machine learning, LLM-based agents, and production engineering, you will own critical architectures from initial design through deployment and optimization. You will also develop evaluation frameworks to measure agent performance and guide data-driven improvements. In a fast-moving, technically ambitious environment, you will collaborate with senior engineers and cross-functional teams to turn emerging AI research into reliable, scalable products. This is an opportunity to establish technical direction, mentor engineering talent, and help redefine how people interact with intelligent assistants throughout their home-search journey.

Architect agent memory systems: Define and own the architecture governing how AI assistants store, retrieve, update, and retire user memories across conversations, ensuring information remains relevant, useful, and reliable over time. Lead context engineering: Design strategies for assembling, prioritizing, and compressing information from conversation histories, user memories, tools, and agent capabilities to generate accurate, relevant, and cost-efficient responses. Build and deploy AI agents: Lead end-to-end development of production-grade agentic systems, covering orchestration, prompt engineering, context design, deployment, monitoring, and ongoing optimization. Develop evaluation frameworks: Establish offline and online evaluation methodologies for memory quality, context relevance, and agent performance, using measurable results to guide technical decisions and product improvements. Advance AI innovation: Explore emerging agent architectures, memory systems, and long-context techniques, translating promising research into practical capabilities that enhance the user experience. Drive technical leadership: Own complex engineering initiatives, influence architecture and implementation decisions across teams, and establish best practices for building and operating reliable machine learning systems. Collaborate cross-functionally: Partner with engineering, product, and other technical stakeholders to align priorities, clarify requirements, resolve dependencies, and deliver scalable solutions. Mentor and develop engineers: Share technical expertise through design reviews, code reviews, and mentoring while helping colleagues strengthen their capabilities in machine learning and agentic AI. Communicate technical direction: Present architectural decisions, evaluation findings, project progress, and strategic recommendations clearly to engineers, partner teams, and senior leadership. Requirements Education: Master's degree or higher in Computer Science, Electrical Engineering, or a related technical discipline, or equivalent practical experience. Machine learning 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. End-to-end agent development: Hands-on expertise building AI agents, including orchestration, prompt engineering and optimization, and context engineering. Evaluation and optimization: Demonstrated experience evaluating agentic systems and applying evaluation results to improve their quality, reliability, and effectiveness. Programming skills: Strong proficiency in Python and familiarity with agentic AI frameworks such as LangGraph, Agents SDK, or AutoGen. Technical leadership: A proven track record of influencing technical direction across multiple teams, leading complex initiatives, and mentoring other engineers. Communication: Strong written and verbal communication skills, including the ability to explain complex technical concepts and present recommendations to senior leadership. Ownership and initiative: A proactive, hands-on approach to problem-solving, with the ability to navigate ambiguity, identify the right stakeholders, remove blockers, and make progress without waiting for detailed instructions. Preferred memory expertise: Experience designing long-term, episodic, or user-profile memory systems for AI agents. Preferred conversational AI experience: Familiarity with dialogue systems that support extended, multi-session conversations and personalize responses using conversation history. Additional advantages: Research publications, patents, or open-source contributions involving agentic AI, memory architectures, or long-context models. Benefits Competitive base 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. Compensation varies by location and depends on factors such as experience and performance. Equity awards: Eligibility for additional equity compensation, with award amounts determined by factors including experience, performance, and location. Remote work flexibility: Fully remote position for eligible U.S.-based employees, with limited location restrictions. High-impact technical work: Opportunities to build and scale advanced AI systems that support personalized experiences for millions of users. Innovation-driven environment: Work with emerging LLM technologies, agentic AI architectures, memory systems, and advanced machine learning techniques. Career growth and leadership: Opportunities to shape technical strategy, lead cross-functional initiatives, and mentor other engineering professionals. Collaborative culture: Work alongside experienced engineers and multidisciplinary teams in an environment that values innovation, inclusion, and continuous improvement. Inclusive employment practices: Equal employment opportunities for qualified applicants, with reasonable accommodations available for individuals who need them.