Principal Machine Learning Engineer

Role Overview

• Define and evolve Wiser's AI and data science technical strategy in partnership with product and business leadership
• Represent Wiser's AI capabilities to customers, partners, and advisors—articulating our approach, roadmap, and differentiation
• Identify high-impact opportunities where AI can solve customer problems or create competitive advantage
• Establish technical standards, patterns, and best practices that influence engineering decisions across the organization
• Architect and build production AI systems including LLM applications, RAG pipelines, semantic search, and traditional ML models
• Design rigorous evaluation frameworks, experimentation methodologies, and monitoring systems that ensure AI solutions deliver reliable, measurable results
• Bridge classical data science approaches (statistical modeling, experimentation design, feature engineering) with modern generative AI techniques
• Own technical quality for AI systems end-to-end: from data pipelines through model deployment to production observability
• Partner with product management to translate business requirements into technical approaches and validate solutions against customer needs
• Mentor and elevate the AI/data science team (3-5 engineers), raising the technical bar through code review, architecture guidance, and knowledge transfer
• Collaborate across engineering teams to integrate AI capabilities into Wiser's broader platform architecture
• Drive build-vs-buy decisions and vendor evaluations for AI infrastructure and tooling
• Champion AI-native development practices across Wiser engineering—demonstrating how AI tools accelerate development, improve code quality, and change what's possible
• Help build an engineering culture where AI augmentation is the default, not the exception

Requirements

• 15+ years of experience in data science, machine learning, or ML engineering, with demonstrated progression into technical leadership
• Deep expertise in statistical methods, experimental design, and classical ML (not just LLM integration)
• Proven ability to architect and deliver production ML/AI systems at scale on cloud platforms (AWS strongly preferred)
• Strong software engineering fundamentals: you write production-quality code, not just notebooks
• Track record of organization-wide technical influence—setting standards, driving architectural decisions, mentoring engineers
• Experience communicating technical strategy and capabilities to non-technical stakeholders, including customers and executives
• Demonstrated ability to operate autonomously, identify high-impact problems, and drive initiatives without close direction
• Active, daily use of AI coding assistants (Cursor, Claude Code, GitHub Copilot) and a demonstrated belief that AI fundamentally changes how software gets built.
• Technical Depth Expected: NLP and text analytics: embeddings, semantic similarity, classification, entity extraction, and information retrieval
• LLM integration patterns: prompt engineering, RAG architectures, agent frameworks, evaluation methods
• Data engineering: pipeline design, data quality, feature stores, and working with large-scale datasets
• MLOps: model deployment, monitoring, A/B testing infrastructure, and production observability
• Python ecosystem mastery; SQL fluency; comfort with distributed systems concepts
• Preferred: Experience with transformer architectures, fine-tuning, or training custom models
• Background in retail, e-commerce, or product data domains
• Familiarity with LLM orchestration frameworks (LangChain, LlamaIndex) and vector databases
• Track record of introducing AI-augmented workflows to teams or organizations
• Opinions (and evidence) on how AI changes engineering practices, team structures, or development processes

Tech Stack

• AWS
• Cloud
• Distributed Systems
• Python
• SQL

Benefits

• Performance-based discretionary bonuses and variable pay plans available for some positions