Staff Machine Learning Engineer, Recommendations

Role overview

A leading online employment marketplace powered by AI-driven matching technology is hiring a Staff Machine Learning Engineer to anchor recommendation capabilities on a two-sided job platform handling billions of interactions. Reporting to the Director of Recommendation Systems, this role partners with engineering and product leadership to shape a multi-year ML roadmap and translate research into production-grade systems. The position carries high visibility across the organization, with responsibility for foundational architecture, mentorship, and end-to-end model ownership.

Responsibilities

- Define and execute the technical vision for core marketplace components, including recommendation engines, matching algorithms, and entity representation platforms - Architect ML systems that handle dynamic interaction prediction, candidate ranking, and retrieval at high throughput and low latency - Address complex two-sided marketplace challenges such as real-time intent prediction, bilateral relevancy, cold-start, and feedback loops between supply and demand - Mentor ML engineers and data scientists across teams, instilling rigor in experimentation and rapid production delivery - Drive end-to-end model ownership from exploration and feature engineering through distributed training, offline and online evaluation, and latency optimization

Requirements

- 8+ years of professional experience developing and deploying ML models in large-scale production environments - Track record of architecting and shipping end-to-end ML solutions that serve production traffic at scale - Deep expertise in recommendation systems, personalization, ranking and retrieval, or interaction prediction - Strong software engineering fundamentals with hands-on experience using PyTorch or TensorFlow - Demonstrated technical leadership and mentorship driving alignment across cross-functional teams - Background in statistical modeling, A/B testing methodology, and offline metric design

Nice to have

- Familiarity with two-sided marketplace dynamics such as supply and demand liquidity, bilateral matching algorithms, dynamic pricing, or auction-based models - Experience with two-tower neural networks, graph neural networks, transformer-based retrieval models, or contextual bandits - MS or PhD in Computer Science, Machine Learning, or a related quantitative field, or equivalent experience - Experience with modern MLOps architectures and distributed training frameworks

Benefits and work setup

- US base salary range of $205,000–$265,000, with potential additional equity, bonuses, or commission as part of total compensation - Comprehensive medical, financial, and other benefits - Flexible vacation and paid time off - Employer-matched 401(k) plan - Hybrid or remote work options available for most US-based positions