Staff Machine Learning Engineer

Indeed•Remote,

6d•$163,000 - $341,000

About The Position

At Indeed, our mission is to help people get jobs. Every search and homepage visit runs a real-time auction that ranks jobs for millions of jobseekers and employers. The ranking utility decides how that auction trades off relevance for jobseekers, value for employers, and revenue for Indeed. As a Machine Learning Engineer III, you will be a team lead on the Utility team in Marketplace Efficiency. You will own the Utility and multi-armed bandit (MAB) workstream. This includes reward design, contextual bandit tuning, arm management, and dynamic filters. You will define the priorities, success metrics, and guardrails that keep exploration safe for jobseekers, employers, and revenue. You will help drive technical direction for the team and guide other members to reach product and technical goals. On a daily basis, you will explore data, formulate optimization problems, and prototype contextual bandit and reward-optimization improvements. You will scale them to production, run high-quality A/B experiments, and monitor results. You will break large initiatives, such as arm management and MAB tuning, into pieces that deliver incremental value, and guide the team on the same. You will also partner with Engineering teams, Data Science, and Product to improve how Indeed ranks jobs. You will explain optimization tradeoffs and experiment results in clear terms, evangelize your team's work, and stay current with advances in the field.

Requirements

Requires a Bachelor's degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 8 years of related experience; or a Master's degree with a minimum of 6 years of experience; or a PhD with 3 years experience
Prior success in deploying impactful Machine Learning solutions to large-scale production systems, while partnering across teams
Solid knowledge of data structures and algorithms
Sense of ownership and accountability as a key contributor in the technical and product domains
Knowledge and practical experience working on Deep Learning Libraries (like Torch, Tensorflow, etc.)
Excellent written and verbal communication in English, effective with technical and business audiences
Direct experience with multi-armed or contextual bandits, reinforcement learning, or reward optimization in production ranking or recommendation systems
Solid foundation in optimization, such as constrained or multi-objective optimization, applied to marketplace, ranking, or advertising problems

Responsibilities

Partner with cross-functional teams to enhance and optimize search algorithms for improved accuracy, relevance, and overall user experience.
Experiment with Proof of Concept Machine Learning model improvements, scale them to production, and run iterative A/B experiments to improve our matching technology while partnering with other teams
Define and clarify project priorities, deliverables, and success criteria in partnership with cross-functional teams.
Act as a bridge between technical and non-technical collaborators, facilitating effective communication and comprehension of project goals and outcomes.
Mentor and grow other software engineers and Machine Learning Engineers across teams
Break down larger Machine Learning initiatives into pieces that deliver incremental business value and guide the team through implementing them
Represent Indeed at major Machine Learning conferences, such as Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the International Learning Representations (ICLR).