Machine Learning Engineer, Risk AI/ML
Coinbase
Machine Learning Engineer, Risk AI/ML
Job Description
Responsibilities
- Focus on Modeling: Use our centralized, self-service ML platform to own the end-to-end development of ML models, from ideation to production.
- Improve Core Model Performance: Join a high-priority "pod" to enhance our core models, including the Scam Models, Transfer/Transaction Risk Models, Withdrawal Limit Models, and Account Takeover models.
- Rapidly Respond to Threats: Act on new threat data (identified by our Risk Operations partners) to build, train, and deploy permanent ML models that replace temporary rules—targeting a deploy-to-production timeline of under one week.
- Build & Deploy Scalable Models: Develop production-grade AI/ML models and pipelines that enable reliable, real-time predictions, leveraging our platform's automated CI/CD pipelines and centralized feature store.
- 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—all while focusing 100% on modeling, not infrastructure plumbing.
- Build Context-Aware Risk Systems: Go beyond a single score. You will help build the adaptive logic that decides which friction (a quiz, an LLM agent, a human review) to apply to which user (e.g., new user, high-value trader), balancing security with user experience.
- Collaborate and Execute: Work closely with stakeholders from Risk Operations, Platform Engineering, and Product Management to close the feedback loop, turning new threats into automated defenses.
Required Qualifications
- 4+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production.
- Passion & Values: 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.
- AI/ML Knowledge: Familiarity with applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection).
- Technical & Coding Skills: Proficient coding skills (e.g., Python) with experience in AI/ML frameworks (TensorFlow, PyTorch). Experience in building backend systems with a focus on data processing or analytics is a plus.
- Team Collaboration: Ability to work collaboratively on technical initiatives and contribute to impactful AI/ML solutions.
- Communication Skills: Strong communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.
Preferred Qualifications
- Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related field.
- Familiarity with modern data and AI/ML infrastructure (e.g., Feature Stores like Tecton, Model Serving like RayServe, Apache Airflow, Spark, Kafka).
- Experience with Graph Neural Networks (GNNs) or Sequential Models (like LSTMs).
- Experience with LLMs (NLP, fine-tuning, agentic systems) or Reinforcement Learning.
- Understanding of MLOps best practices, including monitoring and improving production models.
- Experience with data analysis and visualization tools.
Required Skills
- software engineering
- AI/ML
- deploying AI/ML systems into production
- Python
- TensorFlow
- PyTorch
- deep learning
- NLP
- recommender systems
- anomaly detection
- Risk ML
- backend systems
- data processing
- analytics
- team collaboration
- communication skills
- Graph Neural Networks (GNNs)
- sequence modeling
- LLMs
- MLOps
- data analysis
- visualization tools
- Feature Stores like Tecton
- Model Serving like RayServe
- Apache Airflow
- Spark
- Kafka
- Sequential Models (like LSTMs)
- Reinforcement Learning
Benefits
- bonus eligibility
- equity eligibility
- benefits (including medical, dental, and vision)
Salary Range
$154000 - $154000 CAD
Locations
- Canada, Canada (Remote)
- USA, United States (Remote)
Salary
154,000 - 154,000 CAD / yearly
Skills Required
- software engineeringintermediate
- AI/MLintermediate
- deploying AI/ML systems into productionintermediate
- Pythonintermediate
- TensorFlowintermediate
- PyTorchintermediate
- deep learningintermediate
- NLPintermediate
- recommender systemsintermediate
- anomaly detectionintermediate
- Risk MLintermediate
- backend systemsintermediate
- data processingintermediate
- analyticsintermediate
- team collaborationintermediate
- communication skillsintermediate
- Graph Neural Networks (GNNs)intermediate
- sequence modelingintermediate
- LLMsintermediate
- MLOpsintermediate
- data analysisintermediate
- visualization toolsintermediate
- Feature Stores like Tectonintermediate
- Model Serving like RayServeintermediate
- Apache Airflowintermediate
- Sparkintermediate
- Kafkaintermediate
- Sequential Models (like LSTMs)intermediate
- Reinforcement Learningintermediate
Required Qualifications
- 4+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production. (experience)
- Passion & Values: 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. (experience)
- AI/ML Knowledge: Familiarity with applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection). (experience)
- Technical & Coding Skills: Proficient coding skills (e.g., Python) with experience in AI/ML frameworks (TensorFlow, PyTorch). Experience in building backend systems with a focus on data processing or analytics is a plus. (experience)
- Team Collaboration: Ability to work collaboratively on technical initiatives and contribute to impactful AI/ML solutions. (experience)
- Communication Skills: Strong communication skills, with the ability to convey technical concepts to both technical and non-technical audiences. (experience)
Preferred Qualifications
- Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related field. (experience)
- Familiarity with modern data and AI/ML infrastructure (e.g., Feature Stores like Tecton, Model Serving like RayServe, Apache Airflow, Spark, Kafka). (experience)
- Experience with Graph Neural Networks (GNNs) or Sequential Models (like LSTMs). (experience)
- Experience with LLMs (NLP, fine-tuning, agentic systems) or Reinforcement Learning. (experience)
- Understanding of MLOps best practices, including monitoring and improving production models. (experience)
- Experience with data analysis and visualization tools. (experience)
Responsibilities
- Focus on Modeling: Use our centralized, self-service ML platform to own the end-to-end development of ML models, from ideation to production.
- Improve Core Model Performance: Join a high-priority "pod" to enhance our core models, including the Scam Models, Transfer/Transaction Risk Models, Withdrawal Limit Models, and Account Takeover models.
- Rapidly Respond to Threats: Act on new threat data (identified by our Risk Operations partners) to build, train, and deploy permanent ML models that replace temporary rules—targeting a deploy-to-production timeline of under one week.
- Build & Deploy Scalable Models: Develop production-grade AI/ML models and pipelines that enable reliable, real-time predictions, leveraging our platform's automated CI/CD pipelines and centralized feature store.
- 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—all while focusing 100% on modeling, not infrastructure plumbing.
- Build Context-Aware Risk Systems: Go beyond a single score. You will help build the adaptive logic that decides which friction (a quiz, an LLM agent, a human review) to apply to which user (e.g., new user, high-value trader), balancing security with user experience.
- Collaborate and Execute: Work closely with stakeholders from Risk Operations, Platform Engineering, and Product Management to close the feedback loop, turning new threats into automated defenses.
Benefits
- general: bonus eligibility
- general: equity eligibility
- general: benefits (including medical, dental, and vision)
Tags & Categories
Machine Learning Engineer, Risk AI/ML
Coinbase
Machine Learning Engineer, Risk AI/ML
Job Description
Responsibilities
- Focus on Modeling: Use our centralized, self-service ML platform to own the end-to-end development of ML models, from ideation to production.
- Improve Core Model Performance: Join a high-priority "pod" to enhance our core models, including the Scam Models, Transfer/Transaction Risk Models, Withdrawal Limit Models, and Account Takeover models.
- Rapidly Respond to Threats: Act on new threat data (identified by our Risk Operations partners) to build, train, and deploy permanent ML models that replace temporary rules—targeting a deploy-to-production timeline of under one week.
- Build & Deploy Scalable Models: Develop production-grade AI/ML models and pipelines that enable reliable, real-time predictions, leveraging our platform's automated CI/CD pipelines and centralized feature store.
- 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—all while focusing 100% on modeling, not infrastructure plumbing.
- Build Context-Aware Risk Systems: Go beyond a single score. You will help build the adaptive logic that decides which friction (a quiz, an LLM agent, a human review) to apply to which user (e.g., new user, high-value trader), balancing security with user experience.
- Collaborate and Execute: Work closely with stakeholders from Risk Operations, Platform Engineering, and Product Management to close the feedback loop, turning new threats into automated defenses.
Required Qualifications
- 4+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production.
- Passion & Values: 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.
- AI/ML Knowledge: Familiarity with applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection).
- Technical & Coding Skills: Proficient coding skills (e.g., Python) with experience in AI/ML frameworks (TensorFlow, PyTorch). Experience in building backend systems with a focus on data processing or analytics is a plus.
- Team Collaboration: Ability to work collaboratively on technical initiatives and contribute to impactful AI/ML solutions.
- Communication Skills: Strong communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.
Preferred Qualifications
- Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related field.
- Familiarity with modern data and AI/ML infrastructure (e.g., Feature Stores like Tecton, Model Serving like RayServe, Apache Airflow, Spark, Kafka).
- Experience with Graph Neural Networks (GNNs) or Sequential Models (like LSTMs).
- Experience with LLMs (NLP, fine-tuning, agentic systems) or Reinforcement Learning.
- Understanding of MLOps best practices, including monitoring and improving production models.
- Experience with data analysis and visualization tools.
Required Skills
- software engineering
- AI/ML
- deploying AI/ML systems into production
- Python
- TensorFlow
- PyTorch
- deep learning
- NLP
- recommender systems
- anomaly detection
- Risk ML
- backend systems
- data processing
- analytics
- team collaboration
- communication skills
- Graph Neural Networks (GNNs)
- sequence modeling
- LLMs
- MLOps
- data analysis
- visualization tools
- Feature Stores like Tecton
- Model Serving like RayServe
- Apache Airflow
- Spark
- Kafka
- Sequential Models (like LSTMs)
- Reinforcement Learning
Benefits
- bonus eligibility
- equity eligibility
- benefits (including medical, dental, and vision)
Salary Range
$154000 - $154000 CAD
Locations
- Canada, Canada (Remote)
- USA, United States (Remote)
Salary
154,000 - 154,000 CAD / yearly
Skills Required
- software engineeringintermediate
- AI/MLintermediate
- deploying AI/ML systems into productionintermediate
- Pythonintermediate
- TensorFlowintermediate
- PyTorchintermediate
- deep learningintermediate
- NLPintermediate
- recommender systemsintermediate
- anomaly detectionintermediate
- Risk MLintermediate
- backend systemsintermediate
- data processingintermediate
- analyticsintermediate
- team collaborationintermediate
- communication skillsintermediate
- Graph Neural Networks (GNNs)intermediate
- sequence modelingintermediate
- LLMsintermediate
- MLOpsintermediate
- data analysisintermediate
- visualization toolsintermediate
- Feature Stores like Tectonintermediate
- Model Serving like RayServeintermediate
- Apache Airflowintermediate
- Sparkintermediate
- Kafkaintermediate
- Sequential Models (like LSTMs)intermediate
- Reinforcement Learningintermediate
Required Qualifications
- 4+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production. (experience)
- Passion & Values: 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. (experience)
- AI/ML Knowledge: Familiarity with applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection). (experience)
- Technical & Coding Skills: Proficient coding skills (e.g., Python) with experience in AI/ML frameworks (TensorFlow, PyTorch). Experience in building backend systems with a focus on data processing or analytics is a plus. (experience)
- Team Collaboration: Ability to work collaboratively on technical initiatives and contribute to impactful AI/ML solutions. (experience)
- Communication Skills: Strong communication skills, with the ability to convey technical concepts to both technical and non-technical audiences. (experience)
Preferred Qualifications
- Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related field. (experience)
- Familiarity with modern data and AI/ML infrastructure (e.g., Feature Stores like Tecton, Model Serving like RayServe, Apache Airflow, Spark, Kafka). (experience)
- Experience with Graph Neural Networks (GNNs) or Sequential Models (like LSTMs). (experience)
- Experience with LLMs (NLP, fine-tuning, agentic systems) or Reinforcement Learning. (experience)
- Understanding of MLOps best practices, including monitoring and improving production models. (experience)
- Experience with data analysis and visualization tools. (experience)
Responsibilities
- Focus on Modeling: Use our centralized, self-service ML platform to own the end-to-end development of ML models, from ideation to production.
- Improve Core Model Performance: Join a high-priority "pod" to enhance our core models, including the Scam Models, Transfer/Transaction Risk Models, Withdrawal Limit Models, and Account Takeover models.
- Rapidly Respond to Threats: Act on new threat data (identified by our Risk Operations partners) to build, train, and deploy permanent ML models that replace temporary rules—targeting a deploy-to-production timeline of under one week.
- Build & Deploy Scalable Models: Develop production-grade AI/ML models and pipelines that enable reliable, real-time predictions, leveraging our platform's automated CI/CD pipelines and centralized feature store.
- 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—all while focusing 100% on modeling, not infrastructure plumbing.
- Build Context-Aware Risk Systems: Go beyond a single score. You will help build the adaptive logic that decides which friction (a quiz, an LLM agent, a human review) to apply to which user (e.g., new user, high-value trader), balancing security with user experience.
- Collaborate and Execute: Work closely with stakeholders from Risk Operations, Platform Engineering, and Product Management to close the feedback loop, turning new threats into automated defenses.
Benefits
- general: bonus eligibility
- general: equity eligibility
- general: benefits (including medical, dental, and vision)
Tags & Categories