• *Job Title**
: Lead Applied Machine Learning Scientist
• *Location**
: Remote (Hybrid option available; occasional travel required)
• *About The Company**
This organization is a leading global distributor operating across North America, Europe, and Asia, serving millions of customers through technology-enabled commerce platforms. The company combines large-scale product assortment, deep customer relationships, and advanced digital capabilities to deliver innovative solutions at enterprise scale. The culture emphasizes service excellence, operational rigor, and data-driven innovation, supported by strong investment in advanced analytics and machine learning.
• *Job Summary**
The Lead Applied Machine Learning Scientist will drive the design and deployment of advanced generative AI solutions focused on enhancing product discovery and customer experience. This role leverages Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector retrieval, deep learning, and agentic workflows to build scalable, production-grade machine learning systems.
This is a senior individual contributor role responsible for translating complex customer use cases into robust ML solutions, integrating models into enterprise platforms, and shaping the strategic direction of applied AI initiatives. The ideal candidate combines strong research depth with production engineering experience and business impact orientation.
• *Key Responsibilities
• Generative AI & Agentic Systems: Design, build, and maintain agentic processes that respond intelligently to customer product inquiries. Implement generative AI capabilities using LLMs, RAG architectures, vector search, fine-tuning, and prompt optimization techniques.
• Applied Deep Learning: Utilize deep learning frameworks (e.g., PyTorch, JAX, Keras) to develop and optimize models that improve search relevance, personalization, and product discovery. Develop embeddings and graph-based representations to enhance semantic understanding.
• Production Deployment & Integration: Partner with machine learning engineers, software engineers, and product managers to integrate ML models into production systems. Deploy scalable solutions using containerization and orchestration tools (Docker, Kubernetes) in cloud environments.
• Experimentation & Use Case Development: Work backward from customer use cases to design scalable ML implementations that deliver measurable business impact. Design experiments, evaluate model performance, and iterate to improve outcomes.
• MLOps & Automation: Automate data ingestion, augmentation, and model refresh workflows using tools such as Airflow and scripting frameworks. Ensure reliability, scalability, and reproducibility across ML pipelines.
• Strategic Influence & Thought Leadership: Contribute to the strategic planning of enterprise ML initiatives, aligning technical direction with business objectives. Stay current with advancements in generative AI and machine learning research and apply relevant innovations to production systems.
• Communication & Knowledge Sharing: Clearly articulate technical concepts and model impact to engineering teams and business stakeholders. Create visual representations and documentation to support alignment and adoption. Mentor peers and foster a culture of experimentation and continuous learning.
• *Requirements
• Education: Master’s or PhD degree in Applied Mathematics, Physics, Engineering, Computer Science, Electrical Engineering, or related field (or equivalent professional experience).
• Experience: Minimum of 5+ years delivering machine learning solutions in production environments.
• Generative AI Expertise: Demonstrated experience deploying generative AI solutions in business contexts, including LLMs, RAG architectures, vector retrieval, and agentic workflows. Familiarity with frameworks such as LangChain, LlamaIndex, Autogen, or Crew.ai.
• Deep Learning & Modeling: Hands-on experience with deep learning frameworks such as PyTorch, JAX, or Keras. Strong understanding of model training, fine-tuning, evaluation, and optimization techniques.
• Cloud & Deployment: Experience deploying machine learning models to cloud environments using Docker, Kubernetes, and modern DevOps/MLOps tooling.
• Data Engineering & Automation: Experience automating data pipelines, augmentation workflows, and refresh processes using orchestration tools such as Airflow and scripting languages.
• Communication & Leadership: Ability to effectively communicate complex technical solutions to both engineering and business audiences. Proven ability to influence technical direction and contribute to cross-functional strategic initiatives.
• *Compensation & Eligibility
• Competitive base salary aligned