Senior Software Engineer, Socure

• This role is ideal for someone who enjoys building platforms and tools that abstract complexity for ML and data science teams, and who thrives in fast-paced environments where engineering excellence and reliability are paramount
• 4+ years of experience as a software engineer, with at least 2 years focused on low latency and highly available backend systems
• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field with a strong academic record
• Strong fundamentals in data structures, algorithms, and distributed computing principles
• Strong analytical and problem-solving skills, with a passion for AI and machine learning
• Strong programming skills in Python; familiarity with Go/Rust is a plus
• Hands-on experience with model systems including low latency model serving, registry, and pipeline orchestration(preferably SageMaker)
• Solid understanding of MLOps best practices, including model versioning, testing, deployment, and reproducibility
• Experience building and maintaining CI/CD pipelines for ML workflows
• Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
• Experience with database technologies (SQL, NoSQL, or data warehouses like Snowflake or Redshift)

Desirable

• As a Senior Software Engineer on Socure’s AI Platform team, you’ll design and build infrastructure that supports model training, validation, deployment, and serving at scale
• You will work with modern AWS-native technologies, focusing on low-latency microservices, automated pipelines, and robust deployment workflows to enable safe and efficient delivery of machine learning models into production
• Build and maintain scalable systems and infrastructure for deploying and serving ML models
• Design low-latency, fault-tolerant model inference systems using Amazon SageMaker
• Implement safe deployment strategies like blue/green deployments and rollbacks
• Create and manage CI/CD pipelines for ML workflows
• Monitor model performance and system health using AWS observability tools (e.g., CloudWatch)
• Develop internal tools and APIs to help ML teams deploy and monitor models easily
• Collaborate with ML engineers, data scientists, and DevOps to productionize new models
• Participate in code reviews, system design, and platform roadmap discussions
• Continuously improve deployment reliability, speed, and usability of the ML platform