Senior Machine Learning Engineer, Risk AI/ML
Coinbase
Senior Machine Learning Engineer, Risk AI/ML
Job Description
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.
Required Qualifications
- 6-8+ years of professional 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.
- 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: Deep expertise in applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection).
- Technical & Coding Skills: 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.
- 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
- Master’s / Ph.D 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
- technical leadership
- designing and deploying large-scale ML systems
- Risk ML
- deep learning
- NLP
- recommender systems
- anomaly detection
- Python
- TensorFlow
- PyTorch
- backend systems
- data processing
- analytics
- team collaboration
- communication skills
- Graph Neural Networks (GNNs)
- sequence modeling
- LLMs
- MLOps best practices
- data analysis
- visualization tools
Benefits
- bonus eligibility
- equity eligibility
- benefits (including medical, dental, and vision)
Salary Range
$191100 - $191100 CAD
Locations
- USA, United States (Remote)
Salary
191,100 - 191,100 CAD / yearly
Skills Required
- software engineeringintermediate
- AI/MLintermediate
- deploying AI/ML systems into productionintermediate
- technical leadershipintermediate
- designing and deploying large-scale ML systemsintermediate
- Risk MLintermediate
- deep learningintermediate
- NLPintermediate
- recommender systemsintermediate
- anomaly detectionintermediate
- Pythonintermediate
- TensorFlowintermediate
- PyTorchintermediate
- backend systemsintermediate
- data processingintermediate
- analyticsintermediate
- team collaborationintermediate
- communication skillsintermediate
- Graph Neural Networks (GNNs)intermediate
- sequence modelingintermediate
- LLMsintermediate
- MLOps best practicesintermediate
- data analysisintermediate
- visualization toolsintermediate
Required Qualifications
- 6-8+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production. (experience)
- Proven track record of technical leadership, including designing and deploying large-scale ML systems from scratch. (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: Deep expertise in applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection). (experience)
- Technical & Coding Skills: 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. (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
- Master’s / Ph.D 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
- 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.
Benefits
- general: bonus eligibility
- general: equity eligibility
- general: benefits (including medical, dental, and vision)
Tags & Categories
Senior Machine Learning Engineer, Risk AI/ML
Coinbase
Senior Machine Learning Engineer, Risk AI/ML
Job Description
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.
Required Qualifications
- 6-8+ years of professional 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.
- 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: Deep expertise in applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection).
- Technical & Coding Skills: 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.
- 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
- Master’s / Ph.D 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
- technical leadership
- designing and deploying large-scale ML systems
- Risk ML
- deep learning
- NLP
- recommender systems
- anomaly detection
- Python
- TensorFlow
- PyTorch
- backend systems
- data processing
- analytics
- team collaboration
- communication skills
- Graph Neural Networks (GNNs)
- sequence modeling
- LLMs
- MLOps best practices
- data analysis
- visualization tools
Benefits
- bonus eligibility
- equity eligibility
- benefits (including medical, dental, and vision)
Salary Range
$191100 - $191100 CAD
Locations
- USA, United States (Remote)
Salary
191,100 - 191,100 CAD / yearly
Skills Required
- software engineeringintermediate
- AI/MLintermediate
- deploying AI/ML systems into productionintermediate
- technical leadershipintermediate
- designing and deploying large-scale ML systemsintermediate
- Risk MLintermediate
- deep learningintermediate
- NLPintermediate
- recommender systemsintermediate
- anomaly detectionintermediate
- Pythonintermediate
- TensorFlowintermediate
- PyTorchintermediate
- backend systemsintermediate
- data processingintermediate
- analyticsintermediate
- team collaborationintermediate
- communication skillsintermediate
- Graph Neural Networks (GNNs)intermediate
- sequence modelingintermediate
- LLMsintermediate
- MLOps best practicesintermediate
- data analysisintermediate
- visualization toolsintermediate
Required Qualifications
- 6-8+ years of professional experience in software engineering and/or AI/ML, with experience deploying AI/ML systems into production. (experience)
- Proven track record of technical leadership, including designing and deploying large-scale ML systems from scratch. (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: Deep expertise in applied AI/ML techniques (e.g., Risk ML, deep learning, NLP, recommender systems, anomaly detection). (experience)
- Technical & Coding Skills: 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. (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
- Master’s / Ph.D 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
- 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.
Benefits
- general: bonus eligibility
- general: equity eligibility
- general: benefits (including medical, dental, and vision)
Tags & Categories