Cloud MLOps Engineer supporting Data Science and Engineering teams by automating CI/CD pipelines and managing multi - cloud infrastructure for ML production.
About the role
- Senior Machine Learning Engineer focusing on Risk AI/ML team at Coinbase. Developing sophisticated models to protect customers and prevent fraud.
Responsibilities
- Own a Critical Risk Domain: Take full technical ownership of a core problem space, such as Scams or Account Takeover. You will design, build, and lead the strategy for all models in this domain.
- Architect and Design Systems: Lead the system design and architecture for new, complex risk detection models. This includes everything from feature pipeline design to model selection (e.g., GNNs, LSTMs, LLMs) and high-performance serving.
- Drive the Technical Roadmap: Work with Product, Ops, and other stakeholders to translate ambiguous business needs into a clear technical roadmap. You will be the primary technical voice defining the "how."
- Mentor and Lead: Act as a technical leader and mentor for mid-level and junior engineers on the team. You will lead by example through code reviews, design docs, and coaching.
- Apply Advanced ML: Apply modern methodologies (e.g., deep learning, NLP, Graph Neural Networks (GNNs), sequence modeling, and LLMs for NLP and conversational agents) to solve complex, crypto-native challenges.
- Build Context-Aware Risk Systems: Architect the adaptive logic that decides which friction (a quiz, an LLM agent, a human review) to apply to which user, balancing security with user experience.
Requirements
- 5+ years of working experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production.
- Proven track record of technical leadership, including designing and deploying large-scale ML systems from scratch.
- A commitment to building an open financial system and a strong desire to protect users from fraud and scams. You embody our core cultural values: add positive energy, communicate clearly, be curious, and be a builder.
- Deep expertise in applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection).
- Expert-level coding skills (e.g., Python) and deep experience with AI/ML frameworks (TensorFlow, PyTorch). Experience in building backend systems with a focus on data processing or analytics is a plus.
- Ability to work collaboratively on technical initiatives and contribute to impactful AI/ML solutions.
- Strong communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.
Benefits
- Full time offers include bonus eligibility
- Equity eligibility
- Benefits (including medical, dental, vision and 401(k))
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