Remote ML & GenAI Engineer — Build Scalable AI

Aimlroles; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work

Flexible hours

Job summary

Weave is seeking a Machine Learning Engineer to design and build scalable ML infrastructures, models, and platforms that empower product teams to deliver AI features at scale.

You will work on data pipelines, distributed systems, and cloud deployments across AWS/GCP with a focus on responsible AI and real-world impact. This role involves collaboration with cross-functional teams in a remote environment.

Qualifications

• 3-5 years of experience in Machine Learning or AI, ideally NLP focused.
• Experience building ML-driven B2B multi-tenant applications at scale.
• Strong coding and system design skills with a bias for quality.

Responsibilities

• Design and Develop machine learning infrastructure, tooling, and models to help teams deliver world class experiences.
• Build internal products and platforms to enable teams to incorporate AI into their features and customer facing products.
• Write code that is high-quality, performant, sustainable, and testable.
• Coach and collaborate inside and outside the team to share expertise and promote best practices.
• Work in a cloud environment with distributed components and services.
• Translate product goals into actionable engineering plans for ML features.

Skills

Machine Learning

Distributed systems

Python

LLMs

Data pipelines

Cloud (AWS/GCP)

Docker/Kubernetes

Education

Bachelor's degree in Computer Science or related field

Tools

Dagster

MLFlow

KubeFlow

Triton Server

Postgres

DVC

Git

Job description

Weave is seeking a Machine Learning Engineer to design and build scalable ML infrastructures, models, and platforms that empower product teams to deliver AI features at scale.

You will work on data pipelines, distributed systems, and cloud deployments across AWS/GCP with a focus on responsible AI and real-world impact. This role involves collaboration with cross-functional teams in a remote environment.