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Company Overview
A leading global e-commerce and technology company is focused on enabling customers to live in homes they love. The organization invests heavily in AI and machine learning, developing advanced agentic AI systems across supply chain, customer service, catalog management, and merchandising. The company fosters a culture of innovation, collaboration, and technical excellence to deliver high-impact solutions at scale.
Summary
The Senior Machine Learning Scientist will define and drive the technical strategy, system design, and evaluation frameworks for generative and agentic AI systems. This high-impact role involves shaping AI architectures, mentoring scientists, and delivering production-level AI solutions that influence business outcomes across multiple departments.
Responsibilities
Partner with leadership to set company-wide technical strategy for agentic AI systems, defining architectural standards and best practices
Lead design of agentic AI systems across supply chain, customer service, supplier and carrier outreach, physical retail, catalog management, and merchandising operations
Identify where agentic AI adds value and recommend alternative ML or automation solutions when more appropriate
Design and standardize evaluation frameworks including task success, business impact metrics, quality and failure analysis, human-in-the-loop review, and online/offline evaluation
Establish rigorous approaches to trust, safety, and governance for agentic AI systems
Tech-lead production GenAI products, unifying architectures and implementation patterns to accelerate development and maintenance
Guide teams on agent orchestration, multi-agent coordination, and cross-agent communication
Provide senior-level technical leadership through architecture and code reviews
Collaborate with senior business, product, and technology stakeholders to translate ambiguous problems into AI solutions
Communicate complex GenAI concepts and influence decisions across the organization
Stay current with LLMs, agentic AI, LLMOps, and related advancements, introducing new techniques for business impact
Mentor and grow other ML scientists, raising the overall bar for GenAI excellence
Qualifications
PhD with significant industry experience, or Master’s in Computer Science, AI, ML, or related quantitative field with 10+ years applied ML experience, including senior or staff-level technical leadership
Deep expertise in Generative AI, LLMs, and agentic AI systems with hands-on production experience
Proven ability to design end-to-end AI systems from problem formulation through deployment and evaluation
Strong software engineering skills in Python; familiar with PyTorch, TensorFlow, Langgraph, ADK, data systems, and AIops infrastructure
Experience influencing technical direction across multiple teams without direct authority
Understanding of agent architectures, trade-offs between flexibility and deterministic execution, LLMOps, CI/CD, monitoring, and automated evaluation
Experience building scalable data pipelines and collaborating with platform teams
Track record of designing robust evaluation frameworks for complex, non-deterministic AI systems
Exceptional communication skills for technical and non-technical audiences
Ability to challenge assumptions, push back on stakeholders, and guide teams toward high-value solutions
Passion for mentorship, technical excellence, and building scalable, durable AI systems
Nice to Have
Prior experience defining GenAI or agentic AI strategy at a large organization
Publications or thought leadership in AI, ML systems, or applied GenAI
Experience with AI trust, safety, or governance frameworks
Familiarity with large-scale distributed systems and multi-tenant ML platforms
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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