Machine Learning Engineer II

Indeed•Remote,

4d•$138,000 - $289,000

About The Position

As a Senior Machine Learning Engineer on our Marketplace Efficiency team, you will build the models that make our two-sided marketplace work for employers and job seekers. You will own problems end to end – frame the question, build the model, evaluate it offline, then run a live experiment to measure what changed. The work spans prediction, forecasting, and optimization under uncertainty at global scale. The marketplace reacts to what our models do, so you will reason about cause and effect rather than correlation. You will also apply generative AI where it improves the work, from evaluation tooling to new modeling approaches. The team runs on evidence. Every experiment records its hypothesis, its result, and the question it opens next. You will partner with product, Data Science, and engineering counterparts, and mentor other engineers and scientists. We protect capacity for exploratory research, so part of your time goes to ambitious bets rather than incremental gains.

Requirements

Proficiency in Python for model development and production code
Fluency in SQL to query and analyze large datasets
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.)

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).