Staff Software Engineer, Machine Learning

Match Group • Palo Alto, CA

1d • $265,000 - $280,000 • Hybrid

About The Position

In this position, we are looking for a highly motivated and experienced Staff-level Machine Learning Engineer who operates at the boundary of multiple domains and partners closely with the Director of Engineering. This is a senior individual contributor role for someone who thrives in ambiguity, can step into complex or struggling initiatives across domains, and help drive them back on track through hands-on technical leadership. While most ML engineers are embedded within a single pod, this role is intentionally cross-cutting. You will work across domains (for example, Trust & Safety and Profile, or Recommendations and Growth), identifying gaps, unblocking execution, and setting technical direction where ownership is unclear, or problems span multiple teams.
In this role, you will operate in multiple modes depending on the needs of the organization. At times, you will act as a hands-on technical leader, driving complex initiatives that span multiple teams. At other times, you will embed with a single pod to provide deep technical support and help unblock execution. You will also act as a strategic thought partner to engineering managers—helping shape direction and identify gaps, risks, and opportunities across ML systems that cut across domains. In addition, you will work closely with domain tech leads as a cross-domain advisor, helping bridge architectures, data, and decisions across teams.
This role offers a unique opportunity to gain deep, end-to-end understanding of how machine learning operates across every corner of Tinder’s product and bring strategic view into the team together with engineering manager, while having a direct hands-on impact as IC.

Requirements

• BS/MS in Computer Science or an equivalent field with 8+ years of experience designing, building, and shipping production machine learning systems at scale
• At least two peer-reviewed publications in top-tier conferences or journals (e.g., NeurIPS, ICML, ICLR, KDD, WWW, ACL, CVPR, or equivalent), demonstrating strong ML fundamentals and technical depth
• Strong communication skills, with the ability to explain complex technical concepts clearly to both technical and non-technical audiences
• Professional work experience in Recommendation Systems or Causal Inference (Revenue or Growth)
• Strong hands-on engineering skills, with the ability to write, review, and debug production-quality code and ML pipelines
• Proven track record as a senior individual contributor (Staff or Principal level) of translating complex, ambiguous business problems into Machine Learning problems
• Deep understanding of end-to-end ML systems, including data pipelines, modeling, evaluation, deployment, and monitoring
• Experience of partnering closely with engineering managers and senior stakeholders to lead cross-team initiatives, shape technical direction and execution
• Hands-on experience with the following (or equivalent/similar) tools in production environments: Kubernetes, Triton Inference Server, Ray Serve, Airflow, Flink, or Spark (Databricks)

Responsibilities

• Lead and execute cross-cutting machine learning initiatives that span multiple ML domains, especially where ownership is unclear or problems cut across teams.
• Partner closely with the Director of Engineering to identify the opportunities and set the technical direction of ML team.
• Step into complex or struggling projects, diagnose issues quickly, and help bring them back on track through hands-on technical leadership and execution.
• Embed with individual pods as needed to provide deep technical support, unblock delivery, and raise the quality bar for ML systems and implementations.
• Act as a strategic thought partner to engineering managers, helping shape technical strategy and identify gaps, risks, and opportunities across ML platforms and systems.
• Collaborate with domain tech leads as a cross-domain advisor, aligning architectures, data pipelines, and modeling approaches across teams.
• Influence technical direction and best practices across the ML organization through design reviews, code reviews, and architectural guidance.
• Mentor senior engineers and help develop technical leadership across the team, without direct people management responsibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees