Applied AI/ML Engineer

Summary by AIEngineer.careers

Applied AI/ML Engineer is a Machine Learning Engineer role (full-time). with Atomscale. in BOSTON, US. Compensation shown: $60K–$130K. Imported listing (source: yourcareerhunter.com). Apply on the employer's site (yourcareerhunter.com).

Imported listing (source: yourcareerhunter.com) · Apply on yourcareerhunter.com

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BOSTON · US

$60K–$130K

Full-time

MID

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Imported job description

Sourced from yourcareerhunter.com

Atomscale is hiring an Applied AI/ML Engineer to design, train, validate and deploy machine learning models for materials processing and characterization data. The role builds the infrastructure around models, including evaluation, drift detection and optimization, and involves data engineering to shape datasets feeding model development and production. The stack centers on Python with PyTorch, JAX, vLLM and Ray, plus MLOps tooling such as Weights & Biases and MLflow. This is an in-person role in Boston requiring existing US work authorization.

Responsibilities

Design, train, validate and deploy ML models for materials processing and characterization data
Build model infrastructure including evaluation, drift detection and optimization
Shape datasets and data pipelines that feed model development and production
Ship and maintain model systems using Docker, CI/CD and git

Requirements

Strong applied experience building and deploying machine learning models
Proficiency in Python with PyTorch and/or JAX
Experience with MLOps tooling such as Weights & Biases and MLflow
Familiarity with Docker, CI/CD and git
Must already hold US work authorization (no sponsorship)

Nice to Have

Advanced degree in a relevant field
Experience with vLLM and Ray
Background in materials science or scientific data

Tech Stack

CI/CDDockerGitJAXMLflowPythonPyTorchRayvLLMWeights & Biases

Atomscale

Boston · US · 8+ employees

Atomscale is an AI and materials-science company building frontier intelligence for atomic-scale engineering and advanced-materials manufacturing. Its platform connects and enriches process data, analyzes fabrication and characterization signals in real time, and supports adaptive process control and agentic automation to accelerate materials development and production.