Weave is looking for a Principal Engineer to join the Machine Learning Team specialised in voice and audio modalities, where you will be at the forefront of enabling product innovation and building AI-powered applications. In this role, you will help design how teams across Weave build out AI-powered features, serving as a technical leader who bridges the gap between sophisticated machine learning and practical, customer-facing products.

As a technical cornerstone, you will design the platforms that allow our engineering organisation to incorporate AI into their features seamlessly. Your success is measured by the scalability of our ML infrastructure and the ability of our product teams to deliver world-class, AI-driven experiences to 30,000+ healthcare practices.

This is a strategic leadership position requiring deep expertise in MLOps and GenAI. You will consult with teams on common ML patterns and tradeoffs, ensuring that our technical strategy for data and intelligence positions Weave as a leader in the healthcare communication space.

The Data Fellowship encompasses multiple engineering teams responsible for Weave's core data and AI/ML capabilities. We own or support products such as Call Intelligence and AI Receptionist,t as well as the ML Platform used by all other engineering teams at Weave. You will work across these teams as a technical leader, partnering with Engineering Managers, Staff Engineers, and cross-functional stakeholders to drive architectural coherence and strategic technical initiatives.

• This position is remote (US-based).

• Reports to: Sr Director of Engineering

What You Will Own

Infrastructure & Delivery

Design and develop ML infrastructure, tooling, and models to help teams deliver world-class experiences

Build internal and external products and platforms to enable teams to incorporate AI into their features and customer-facing products.

Translate product goals into actionable engineering plans and build scalable, resilient services for data integration and event processing.g

Cross-Team Problem Solving

Help product and development teams understand the data lifecycle and consult with teams on common ML patterns/tradeof.fs.

Coach and collaborate inside and outside the team to elevate technical standards

Write high-quality, performant, sustainable, and testable code while working in a cloud environment.

Strategic Technical Leadership

Monitor the industry landscape, anticipate where technological advances are heading, and ensure Weave stays ahead of the curve.e

Cut through noise and hype to identify genuine strategic value; advocate for and lead key initiatives that prepare Weave for emerging challenges

Shape company-wide standards for engineering excellence, observability, and reliability in distributed systems.

Mentorship & Organisational Capability

Actively mentor Staff and Senior Engineers across the fellowship, developing the next generation of technical leaders

Elevate architectural thinking across teams through design reviews, documentation standards, and hands-on guidance

Build organisational capability that persists beyond your individual contributions

What You Will Need to Accomplish the Job

12+ years of software engineering experience with progressive technical leadership scope

Demonstrable experience building and deploying ML-driven B2B multi-tenant applications in production environments at scale for external products and customers

Deep expertise in distributed systems architecture, including building and operating services that handle hundreds of millions of transactions and terabytes of data

15+ years of experience in Machine Learning or AI, with a focus on or expertise in audio and voice GenAI solutions at scale

Deep expertise with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, and specifically high-scale audio/voice models and LLM evaluations

Strong background in scalable data stores—both relational (PostgreSQL at scale, Vitess, Spanner) and NoSQL (Bigtable, Redis, etc.)

Operational experience with cloud-native infrastructure on GCP or AWS, including Kubernetes, infrastructure-as-code, and highly available system design