Foundation EGI

AI-powered platform for manufacturing optimization

ML Ops Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeBoston, MA, USA$120k - $160k /yrRemote

About the Job

Requirements

Experience in computer graphics or physics-based simulation.
Experience setting up Prometheus/Grafana, ELK, or similar monitoring stacks.
Experience with Vertex AI.
Experience working with custom domain-specific languages.
A Bachelor of Science degree in Computer Science or a related field.
At least 5 years of experience as an AI/ML Ops, DevOps, Infrastructure Engineer, or equivalent.
Expert-level Python and TypeScript skills.
Experience with Docker, Kubernetes, Terraform, Google Cloud, and AWS.
Deep understanding of machine learning models, including large language models.
Experience designing and maintaining continuous integration and continuous delivery pipelines to fine-tune or train machine learning models.

Responsibilities

Architect, build, and operate end-to-end machine learning pipelines for training, validation, and deployment on Google Cloud and AWS.
Define, instrument, and maintain logging, monitoring, and alerting for model performance and data drift.
Automate continuous integration and continuous delivery for machine learning artifacts and infrastructure using GitHub Actions or an equivalent tool.
Collaborate with cross-functional teams, including frontend, backend, research, and infrastructure engineers.
Write clean, well-documented, fast, and maintainable code.
Help ensure systems have high availability and performance.

Desired Qualifications

Experience in computer graphics or physics-based simulation.
Background in setting up Prometheus/Grafana, ELK, or similar monitoring stacks.
Experience with Vertex AI.
Experience working with custom domain-specific languages.

Salary Range: 120000 to 160000 (Currency: USD) (Pay period: per-year-salary)

Requirements:

Architect, build, and operate end-to-end ML pipelines for training, validation and deployment on Google Cloud and AWS.
Define, instrument, and maintain logging, monitoring, and alerting for model performance and data drift.
Automate CI/CD for ML artifacts and infrastructure using GitHub Actions or equivalent.
Collaborate with cross-functional teams, including frontend engineers, backend engineers, research engineers, and infrastructure engineers.
Write clean, well-documented, fast, and maintainable code.
Help ensure our systems have high availability and performance.
Experience in computer graphics or physics-based simulation.
Background in setting up Prometheus/Grafana, ELK, or similar monitoring stacks.
Experience with Vertex AI.
Experience working with custom Domain-Specific Languages.

About Us:

We are an MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'—an AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process.

What we're looking for

BS in Computer Science or a related field.
5+ years of experience as a AI/ML Ops, DevOps, Infrastructure Engineer or equivalent.
Expert-level Python and TypeScripts skills.
Experience with Docker, Kubernetes, Terraform, Google Cloud and AWS.
Deep understanding of machine learning models, including LLMs.
Experience designing and maintaining CI/CD pipelines to fine-tune or train ML models.
Excellent written and verbal communication skills.

Bonus Points

Experience in computer graphics or physics-based simulation.
Background in setting up Prometheus/Grafana, ELK, or similar monitoring stacks.
Experience with Vertex AI.
Experience working with custom Domain-Specific Languages.

Our tech stack

Google Cloud, AWS
Python, TypeScript
Protobuf, gRPC
Next.JS, React.JS
GitHub Actions
Docker, Kubernetes, Spinnaker
PostgreSQL

About the Company

Foundation EGI

AI-powered platform for manufacturing optimization

Foundation EGI builds an AI platform for manufacturing SaaS that uses a purpose-built LLM and agent-based AI to optimize the entire design-to-production lifecycle. The platform plugs into existing manufacturing workflows, analyzes data across design and production, and can automate actions or provide actionable recommendations to improve throughput, quality, and efficiency. It differentiates itself by leveraging deep expertise in computer graphics, computational design, and machine learning to offer an enterprise-grade, end-to-end solution tailored to manufacturing, not generic tools. Its goal is to modernize American manufacturing by delivering scalable AI-powered tools that streamline design-to-production processes and enable data-driven decision-making.

Company size: 11-50
Company stage: Series A
Total funding: $36.6M
Headquarters: Los Altos, CA
Founded: 2023

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