Senior Machine Learning Engineer, Gen AI

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 100M’s to 10B’s of 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, multi-modal 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 have experience working in enterprise-grade production environments.

Strategic thinker with a strong technical aptitude and a passion for execution.

A background with 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, 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.

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