Staff Machine Learning Engineer
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About the Role
At eBay, Risk & Compliance plays a critical role in protecting the integrity of our global marketplace and ensuring trust for millions of buyers and sellers worldwide. Our mission spans fraud prevention, regulatory compliance, financial crime, abuse detection, and policy enforcement—operating at massive scale and under constantly evolving regulatory requirements.
We are seeking a Senior Staff Machine Learning Engineer to serve as a technical leader within the Risk & Compliance organization. This role is responsible for driving the architecture and delivery of end-to-end AI systems, with a strong emphasis on Generative AI and agentic AI, to address complex, high-impact risk problems across the platform.
This is a senior technical role with significant autonomy and influence, requiring deep hands-on expertise, strategic thinking, and the ability to lead initiatives that span multiple teams and domains.
Primary Job Responsibilities
As a Senior Staff Machine Learning Engineer in Risk & Compliance, you will operate as a technical authority and multiplier. Your responsibilities include, but are not limited to:
Own and drive the technical vision for large-scale ML and AI systems supporting fraud detection, risk assessment, compliance enforcement, and policy automation.
Architect and deliver end-to-end AI solutions, from data strategy and feature engineering through model training, deployment, and real-time or batch inference.
Lead the design and implementation of Generative AI and agentic AI systems, including LLM-based decision support, autonomous investigation agents, workflow orchestration, and human-in-the-loop systems.
Translate ambiguous regulatory, policy, and business requirements into scalable, reliable, and explainable ML architectures.
Design and optimize high-throughput ML and data pipelines operating on massive, heterogeneous datasets across distributed systems.
Partner closely with applied researchers, policy teams, legal, operations, and platform engineering to ensure AI solutions are effective, compliant, and production-ready.
Act as a technical owner across multiple initiatives, setting architectural standards, reviewing designs, and influencing long-term platform direction.
Drive MLOps best practices including model lifecycle management, automated testing, monitoring, retraining, and governance.
Ensure systems meet risk, compliance, and responsible AI requirements, including robustness, auditability, fairness, and transparency.
Mentor senior engineers and ML practitioners, raising the technical bar and fostering a culture of engineering excellence.
Required Skills and Experience
Bachelor’s, Master’s, or PhD in Computer Science or a related technical field, or equivalent practical experience.
8+ years of industry experience building, deploying, and operating machine learning systems at scale.
Demonstrated success delivering end-to-end ML and AI solutions in production, including ownership of system architecture and long-term evolution.
Deep expertise in Machine Learning, Deep Learning, and NLP, with hands-on experience in Generative AI (LLMs, RAG, prompt engineering, evaluation).
Experience designing or implementing agentic AI systems, such as autonomous agents, tool-using agents, multi-step reasoning, or decision orchestration frameworks.
Strong background in distributed systems and large-scale data processing (e.g., Spark, Hadoop, streaming platforms).
Proficiency in Python and at least one additional language such as Java or Scala.
Excellent system design skills, with a track record of building scalable, reliable, and maintainable production systems.
Experience with MLOps, including CI/CD for ML, model monitoring, performance tracking, and governance workflows.
Proven ability to lead through influence, communicate complex ideas clearly, and collaborate effectively across engineering, product, and non-technical stakeholders.
Nice to Have
Experience applying ML or AI to fraud detection, financial crime, abuse prevention, or regulatory compliance.
Hands-on experience deploying LLMs on GPU-based or distributed inference infrastructure.
Prior experience in large-scale marketplaces, payments, or consumer-facing platforms.
The base pay range for this position is expected in the range below:
$136,000 - $228,600Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience. The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable) in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
If hired, employees will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
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