Principal, Machine Learning Engineer (L5)

Accountabilities: Lead AI/ML innovation: Design, develop, and deliver advanced machine learning capabilities for intelligent customer engagement, supporting new functionality within a rapidly evolving product environment. Drive research and experimentation: Translate ambiguous business and technical challenges into structured research initiatives, design rigorous experiments, and validate hypotheses through rapid development cycles. Advance LLM and contextual intelligence capabilities: Investigate and implement state-of-the-art LLM orchestration, embedding models, retrieval strategies, vector stores, and data-driven personalization techniques. Build production-grade ML systems: Architect, develop, and deploy scalable machine learning services capable of processing high-volume data and supporting streaming and real-time inference workloads. Establish technical excellence: Define architectural standards, engineering best practices, and design principles that improve system reliability, maintainability, performance, and scalability. Provide technical leadership and mentorship: Guide engineers through technical reviews, architectural decisions, knowledge sharing, and mentoring, raising the team's capabilities without requiring formal management authority. Leverage modern AI development tools: Identify and integrate emerging AI frameworks and development platforms to accelerate implementation, reduce repetitive engineering work, and focus resources on high-value innovation. Collaborate across teams: Work closely with cross-functional stakeholders to translate complex requirements into effective technical solutions and communicate research findings, trade-offs, and implementation strategies. Continuously evolve technical expertise: Stay informed about advances in machine learning, generative AI, autonomous agents, and personalization, applying relevant developments to real-world product challenges. Requirements Professional experience: At least 10 years of applied machine learning or artificial intelligence experience, with a proven track record of independently owning technically ambitious systems from conception through production deployment. Technical leadership: Demonstrated ability to operate in ambiguous, early-stage product environments, take initiative, make informed decisions, and deliver results without waiting for fully defined requirements. Programming expertise: Strong proficiency in Python and experience developing robust, maintainable software for complex machine learning applications. Machine learning foundations: Deep understanding of statistical machine learning algorithms, transformer architectures, large language models, and modern AI techniques. Production AI/ML architecture: Extensive experience designing, architecting, and deploying production-grade AI/ML systems, including LLM orchestration, embedding models, and vector databases. Cloud engineering: Hands-on experience building cloud-based services on AWS, Google Cloud Platform (GCP), or Microsoft Azure. Data-intensive systems: Experience working with high-volume datasets, streaming pipelines, real-time inference systems, and diverse data storage technologies. Technical influence: Ability to elevate engineering standards through mentorship, design reviews, architectural guidance, and effective knowledge sharing. Communication and collaboration: Excellent communication skills, sound technical judgment, and the ability to work effectively with multidisciplinary and geographically distributed teams. Preferred experience: Previous work developing autonomous agents capable of managing multi-turn conversations while maintaining long-term context and consistency. Predictive modeling expertise: Experience modeling user behavior, designing experiments, and applying causal or counterfactual inference methods when full randomization is not possible. Global collaboration: Experience working within distributed teams across different locations and time zones. Benefits Competitive compensation aligned with experience, skills, and local market conditions. Remote-first working arrangements for eligible employees based in India. Generous paid time off to support rest, personal commitments, and work-life balance. Parental leave and additional wellness leave. Healthcare benefits, with specific offerings depending on location. Retirement savings programs, subject to local eligibility and plan availability. Opportunities to work on advanced AI/ML technologies and solve complex, large-scale engineering challenges. A collaborative, globally distributed environment that encourages innovation, continuous learning, and professional growth. Opportunities to influence technical strategy, mentor talented engineers, and contribute to high-impact AI-powered products. Support for employee volunteering and charitable giving initiatives. Occasional travel for team gatherings, customer meetings, and in-person collaboration may be required.