6 months ago
Responsibilities
- Design and develop machine learning infrastructure, tooling, and models.
- Build internal products and platforms that enable teams to incorporate AI into customer-facing features.
- Help product and development teams understand the machine learning data lifecycle, patterns, anti-patterns, and tradeoffs.
- Build scalable and resilient services for data integration, event processing, and platform extensions.
- Evolve product functionality that serves large amounts of data and traffic.
- Write high-quality, performant, sustainable, and testable code.
- Coach and collaborate with teammates and stakeholders while promoting best practices.
- Design functionality across distributed cloud components and services.
- Translate product goals into actionable engineering plans with stakeholders.
Requirements
- 5+ years of experience with a structured backend language such as Go, Java, or Python.
- Experience moving and storing terabytes of data or hundreds of millions to tens of billions of records.
- Experience building and deploying production ML-driven B2B multi-tenant applications.
- Experience with ML technologies including Python, Jupyter, workflow engines, DVC, Triton Server, LLMs, and Postgres.
- Experience with LLMs, RAG, prompt engineering, fine-tuning, and multimodal models.
- Experience with data labeling or annotation for audio or text use cases.
- Understanding of distributed systems and scalable, redundant, observable services.
- Expertise designing and architecting systems for distributed datasets and services.
- Experience building solutions on public clouds such as AWS or GCP.
- Experience providing stable libraries and SDKs for internal use.
- Demonstrated delivery of complex projects in enterprise-grade production environments.
- Preferred: 3+ years of experience in data science, machine learning, or predictive analytics in addition to engineering experience.
- Preferred: experience with natural language models, embeddings, and production-scale inference.
- Preferred: experience with real-time audio models, transcription, ASR pipelines, interruption detection, audio alignment, and speech synthesis.
- Preferred: experience with Model Context Protocol, Kubernetes or GKE, the Operator Pattern, containers, workflow engines, sensitive PHI and PII data, GitOps, infrastructure as code, and configuration-driven systems.
- Preferred: understanding of streaming, data mesh, data lakes, warehouses, or distributed machine learning.
Benefits
- Fully remote within the United States, with optional office work for employees near the Lehi, Utah headquarters.
Tech Stack
Categories
About Weave
Weave builds software for small and medium-sized healthcare practices—especially dental and optometry—that unifies phones, texting, online scheduling, digital forms, payments, and reviews to manage patient communication and operations. It sells its platform as SaaS with industry-specific tools and integrations. Founded in 2008 and headquartered in Lehi, Utah, Weave is a public company listed on the NYSE.