Weave is hiring a Senior Machine Learning Engineer to join our Machine Learning Team. You will enable product innovation by making it painless for developers to build AI-powered applications requiring large data sets. You'll build models with emerging technologies at scale, handling data for hundreds of millions of people daily.

What You'll Do

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 tradeoffs of machine learning.
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.
Coach and collaborate inside and outside the team.
Work in a cloud environment, considering functionality implementation through distributed components and services.
Work with stakeholders to translate product goals into actionable engineering plans.

What We're Looking For

High integrity, team-focused approach, and collaboration skills.
Responsive person with a strong bias for action.
5+ years of experience in Machine Learning or AI, preferably with a focus on natural language.
Experience moving and storing TBs of data or 100M’s to 10B’s of records.
Experience building and deploying ML-driven B2B multi-tenant applications in production environments at scale.
Experience with common ML technologies such as Python, Jupyter, Workflow Engines (Dagster, MLFlow, KubeFlow), DVC, Triton Server, LLMs, Postgres.
Experience with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, LLM evaluations, multi-modal models.
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 systems for distributed data sets and services.
Experience building solutions to run on one or more of the public clouds (e.g., AWS, GCP).
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.
Demonstrated capacity for leadership or mentorship.
Strategic thinker with a strong technical aptitude and a passion for execution.

Nice to Have

A background with data analysis, visualization, and presentation.
3+ years of experience in engineering and systems with strong proficiency in coding and system design.
Experience with low latency natural language models and pipelines 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. Experience with Kubernetes or GKE and the Operator Pattern (GCP) 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.
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.

Technical Stack

Python, Jupyter
Workflow Engines (Dagster, MLFlow, KubeFlow, etc)
DVC, Triton Server, LLMs, Postgres
AWS, GCP
Kubernetes, GKE

Team & Environment

You'll be part of cross-functional agile teams composed of a product owner, backend and frontend developers, and DevOps engineers, reporting to an Engineering Director.