Staff Machine Learning Engineer, Fraud Data
Plaid
AirflowPythonPyTorchSpark
Senior Machine Learning Engineer – Data Foundation, AI
Plaid
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • 🇺🇸 United States
Visit company websiteSalary
💰 $225,600 - $337,200 per year
Job Level
Senior
Tech Stack
Distributed SystemsPythonPyTorch
About the role
- Design, build, and scale advanced ML/AI systems that power Plaid products and applications.
- Develop reliable distributed training and serving systems.
- Improve ML operations at scale.
- Build AI-powered applications that enable new product experiences.
- Collaborate with team members and experiment with cutting-edge ML and AI techniques.
Requirements
- 5+ years of experience training, deploying, and scaling ML/AI models in production environments.
- Strong experience with distributed systems and ML operations — from large-scale training to low-latency serving and monitoring.
- Proficiency in Python and modern ML frameworks (e.g., PyTorch), with the ability to implement and optimize complex models.
- Hands-on experience building or scaling ML/AI infrastructure, pipelines, or reusable platforms that support multiple teams.
- Curiosity and drive to experiment with advanced AI techniques (e.g., embeddings, retrieval, generative modeling) while staying grounded in production impact.
- Experience applying ML/AI in fintech or similarly regulated industries is a plus.
- Ability to thrive in a collaborative environment, working with both technical and non-technical partners to drive measurable outcomes.
Benefits
- Comprehensive benefit plan, including medical, dental, vision, and 401(k)
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
ML/AI systemsdistributed systemsML operationsPythonPyTorchmodel optimizationML/AI infrastructureML pipelinesembeddingsgenerative modeling
Soft skills
collaborationcuriositydriveexperimentationcommunicationworking with technical partnersworking with non-technical partnersmeasurable outcomes
