Remote ML Engineer — Real-Time Pipelines & Distributed Models

StackAdapt Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

StackAdapt is seeking a Machine Learning Engineer to join its Data Science team. You will design modular, real-time data pipelines to handle massive datasets and implement custom ML algorithms in a low-latency environment.

You will work with microservices that run training, inference, and monitoring on thousands of ML models in a scalable distributed system. A strong CS background and high GPA are valued in our remote-friendly, global team.

Qualifications

• Design modular and scalable real-time data pipelines to handle huge datasets.
• Understand and implement custom ML algorithms in a low latency environment.
• Work on microservice architectures that run training, inference, and monitoring on thousands of ML models concurrently.
• Have the ability to take an ambiguously defined task, and break it down into actionable steps.
• Have deep understanding of algorithm and software design, concurrency, and data structures.
• Enjoy working in a friendly, collaborative environment with others.

Responsibilities

• Design modular and scalable real-time data pipelines to handle huge datasets
• Understand and implement custom ML algorithms in a low latency environment
• Work on microservice architectures that run training, inference, and monitoring on thousands of ML models concurrently

Skills

Real-time pipelines

ML algorithms

Microservice architectures

Distributed systems

Ambiguity to action

Collaborative environment

Education

High GPA CS program

Job description

StackAdapt is seeking a Machine Learning Engineer to join its Data Science team. You will design modular, real-time data pipelines to handle massive datasets and implement custom ML algorithms in a low-latency environment.

You will work with microservices that run training, inference, and monitoring on thousands of ML models in a scalable distributed system. A strong CS background and high GPA are valued in our remote-friendly, global team.