Senior Machine Learning Engineer

Our Mission

As the world's number 1 job site*, our mission is to help people get jobs. We strive to cultivate an inclusive and accessible workplace where all people feel comfortable being themselves. We're looking to grow our teams with more people who share our enthusiasm for innovation and creating the best experience for job seekers.

(*Comscore, Total Visits, March 2026)

Day to Day

At Indeed, our mission is to help people get jobs. Every search and homepage visit runs a real-time auction that ranks sponsored and organic jobs for millions of jobseekers. A common ranking utility decides that order across Indeed and its partner surfaces.

As a Senior Machine Learning Engineer on the Utility team in Marketplace Efficiency, you will build the models and tools behind that utility. You will work on dynamic filters that adapt to each request and on models for Multi-Armed Bandit or Utility algorithms. You will build scalable, high-performance components that rank millions of jobs for millions of jobseekers in real time. You will also build and maintain request-level simulation tooling. This tooling lets scientists replay production traffic and test ranking, utility, and filter changes offline before a live experiment.

On a daily basis, you will explore data to define problem statements, build and tune models and algorithms, and write production code. You will scale model, algorithms and filter improvements to production, run A/B experiments to measure their impact, and monitor performance. You will do science research and run experiments that help optimize the outcomes for Employers, Job Seekers and Marketplace. You will collaborate with engineers, scientists, and product managers, and stay current with advances in the field.

Responsibilities

Build highly scalable and performant recommendation systems and technologies that can identify optimum matches between millions of jobs and millions of job seekers in real-time.

Experiment with Proof-Of-Concept Machine Learning (POC ML) model improvements, scale them to production, and run iterative A/B experiments to improve our matching technology.

Mentor other software engineers, data scientists, and Machine Learning Engineers in the team.

Break down larger Machine Learning initiatives into pieces that deliver incremental business value and implement them.

Represent Indeed at major Machine Learning conferences like Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), and International Conference on Learning Representations (ICLR).

Skills/Competencies

Requires a Bachelor's degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 5 years of related experience; or a Master's degree with a minimum of 3 years of experience; or a PhD without experience

Prior success in deploying impactful Machine Learning solutions to large-scale production systems

Solid knowledge of data structures and algorithms

Exceptional sense of ownership

Excellent written and verbal communication in English, effective with technical audiences

Knowledge and practical experience working on Deep Learning Libraries (like Torch, Tensorflow, etc.)

Experience building simulation, offline evaluation, or counterfactual estimation tooling for ranking, recommendation, or advertising systems

Proficiency in Python and SQL for model development and large-scale data analysis.

Salary Range Transparency

Tier 1 - United States of America 138,000 - 208,000 USD per year

Tier 2 - United States of America 154,000 - 230,000 USD per year

Tier 3 - United States of America 169,000 - 253,000 USD per year

Tier 4 - United States of America NA

Tier 5 - United States of America 193,000 - 289,000 USD per year

Salary Range Disclaimer

The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job-related skills, experience, and expertise, as evaluated during the interview process. The range(s) listed is just one component of Indeed's total compensation package for employees. Other rewards may include quarterly bonuses, Restricted Stock Units (RSUs), a Paid Time Off policy, and many region-specific benefits. Compensation may also vary based on where a role is performed, as work locations are grouped into geograp