Senior Applied ML Scientist — Remote, Scale & Impact

StackAdapt Inc.; United States; Remote; USD 160,000 - 220,000; Full time

Benefits offered by this job

Competitive salary

401K/Pension Savings

Paid time off

Job summary

StackAdapt is seeking a Senior/Staff Applied Machine Learning Scientist to join our engineering team. You will lead the development of production-grade ML models, optimizing algorithms to boost advertising ROI and scale to millions of requests per second.

The role emphasizes end-to-end model development, from prototyping to deployment, in a remote-friendly US/Canada environment. Strong ML, statistics, and coding skills are essential, with collaboration across ML Engineers and data scientists.

Qualifications

• 3+ years of industry experience.
• Masters degree or PhD in Computer Science, Statistics, Operations Research, or a related field.
• Ability to break down ambiguously defined tasks into actionable steps.
• Strong understanding of statistics, optimization and machine learning.
• Proficiency in coding, data structures and algorithms.

Responsibilities

• Lead the creation and optimization of advanced ML algorithms to enhance advertising effectiveness and ROI using deep ML expertise.
• Own end-to-end development of production-grade ML models and deploy into live systems with ML Engineers.
• Prototype and rigorously test innovative algorithms and data pipelines using historical data to validate performance and scalability.

Skills

Deep ML expertise

Production-grade ML models

Proficient coding

Statistics & optimization

Data pipelines

Education

Master's degree or PhD in CS/Statistics/OR

Tools

Python

TensorFlow/PyTorch

Job description

StackAdapt is seeking a Senior/Staff Applied Machine Learning Scientist to join our engineering team. You will lead the development of production-grade ML models, optimizing algorithms to boost advertising ROI and scale to millions of requests per second.

The role emphasizes end-to-end model development, from prototyping to deployment, in a remote-friendly US/Canada environment. Strong ML, statistics, and coding skills are essential, with collaboration across ML Engineers and data scientists.