Senior ML - GenAI Engineer, Voice & Speech

Weave is looking for a Senior ML - GenAI Engineer, Voice & Speech to join their team.

The Machine Learning Team's mission is to enable product innovation by making it painless for developers to build AI-powered applications that require access to large sets of data.

As a Machine Learning Engineer on the team, you'll be building models for new products with emerging technologies, at scale.

Responsibilities

• Design and develop machine learning infrastructure, tooling, and models to help teams deliver world-class experiences.
• Help product and development teams understand the data lifecycle and the inherent experimental nature of machine learning.
• Build internal products and platforms to enable teams to incorporate AI into their features and customer-facing products.
• Consult with teams to help them understand common patterns, anti-patterns, and trade-offs of machine learning.
• Guide them through creating excellent customer experiences end to end.
• Build scalable, resilient services to support data integration, event processing, and platform extensions.
• Contribute to the continued evolution of product functionality that services large amounts of data and traffic.
• Write code that is high-quality, performant, sustainable, and testable while holding yourself accountable for the quality of the code you produce.
• Coach and collaborate inside and outside the team.

Requirements

• High integrity, team-focused approach, and collaboration skills to build tight-knit relationships across Weave with various roles and stakeholders.
• Responsive person with a strong bias for action.
• 5+ years of experience in any structured back-end language, i.e. Go, Java or Python (Go and Python experience is a plus).
• Experience moving and storing TBs of data or 100 M to 10 B records.
• Experience building and deploying ML-driven B2B multi-tenant applications in production environments.
• Experience with common ML technologies such as Python, Jupyter, Workflow Engines (Dagster, MLflow, Kubeflow, etc.), DVC, Triton Server, LLMs, Postgres, and others.
• Experience with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, fine-tuning, multimodal models, and others.
• Experience with data labelling or annotation for audio or text use cases.
• Understanding of distributed systems and building scalable, redundant, and observable services.
• Expertise in designing and architecting systems for distributed data sets and services.
• Experience building solutions to run on one or more of the public clouds (e.g., AWS, GCP, etc.).
• Experience providing stable, well-designed libraries and SDKs for internal use.
• Self-driven and a thirst for learning in a quickly changing industry.
• Demonstrated track record of delivering complex projects on time and experience working in enterprise-grade production environments.
• Strategic thinker with a strong technical aptitude and a passion for execution.

Nice to Have

• A background in data analysis, visualization, and presentation.
• 3+ years of experience in data science, machine learning, or predictive analytics in addition to engineering experience.
• Experience with natural language models, embeddings, and inference in production, at scale.
• Experience with real-time audio models and voice use cases such as transcription, ASR pipelines with interruption detection, audio alignment, and speech synthesis.
• Experience with emerging technologies such as Model Context Protocol (MCP).
• Proficient understanding of containers, orchestrators, and usage patterns at scale, including networking, storage, service meshes, and multi-cluster communication.
• Experience with Kubernetes or GKE and the Operator Pattern (GCP), specifically, is a plus.
• Experience with highly sensitive data such as PHI (HIPAA) and PII data.
• Experience with automation and container-based workflow engines.
• Experience with GitOps, IaC, and configuration-driven systems.
• A preference for open source solutions.
• A track record of clean abstractions and simple-to-use APIs.
• Deep understanding of distributed data technologies such as streaming, data mesh, data lakes, warehouses, or distributed machine learning.
• A desire to advance the state of the art with new and innovative technologies.
• Enjoys working in a greenfield environment using rapid prototyping.
• Enjoys working with open-ended, evolving problems and domains.