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 is the all-in-one experience platform for small- and medium-sized healthcare businesses. From the first phone call to the final invoice and every touchpoint in between, Weave connects the entire customer journey. Weave’s software solutions transform how healthcare businesses attract, communicate with, and engage patients to grow their business. In the past year, Weave has been named a G2 leader in Patient Engagement, Optometry, Dental Practice Management, and Patient Relationship Management software. Beware of recruitment fraud: All official correspondence will occur through Weave branded email. We will never ask you to share bank account information, cash a check from us, or purchase software or equipment as part of your interview or hiring process.