Principal Machine Learning Engineer

Wiser Solutions•Boston, MA

4h•$155,000 - $170,000•Remote

About The Position

Wiser Solutions is seeking a Principal Machine Learning Engineer to shape and execute our AI and data science strategy. This is a senior technical leadership role for someone who combines deep expertise in machine learning, data science, and production engineering with the business acumen to translate complex capabilities into customer value. You will be the technical authority for AI at Wiser: defining architectural direction, representing our capabilities to customers and partners, and delivering production systems that drive measurable business outcomes. This role requires someone who operates fluidly between strategic planning and hands-on implementation, who can present to executives and debug production pipelines in the same week. We're building an AI-native engineering culture at Wiser, one where AI tools and techniques are woven into how we work, not just what we build. We need a Principal AI Engineer who doesn't just deliver AI products but models AI-augmented ways of working and helps the broader engineering organization adopt them. If you believe AI is fundamentally transforming software development and you're already living that transformation daily, we want to talk.

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
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

Nice To Haves

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

Responsibilities

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