Principal Machine Learning Engineer (Memory & Context)

Accountabilities: 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.