Senior Backend Software Engineer

Unlearn•San Francisco, CA

About The Position

We are seeking a Senior Backend Software Engineer to play a hands-on role in building and optimizing backend systems for our applications while also contributing to infrastructure for ML model training and production. This role requires strong expertise in backend development, distributed systems, and cloud infrastructure. The ideal candidate will work closely with ML engineers, data scientists, and product teams to ensure scalable, efficient, and high-performance systems.

Requirements

7+ years of backend software engineering experience, with expertise in designing and building scalable, distributed systems.
Strong proficiency in Python (or other backend languages such as Go, Java, or Rust).
Deep experience with databases (SQL, NoSQL), API design (REST, GraphQL), and message queues (Kafka, RabbitMQ).
Hands-on experience with cloud infrastructure (AWS or GCP) and containerization (Docker, Kubernetes).
Familiarity with ML model deployment workflows and experience integrating models into production environments.
Strong knowledge of CI/CD, DevOps practices, and infrastructure as code (Terraform, Ansible, or similar).
Strong problem-solving skills and ability to optimize system performance and reliability.
Excellent communication and collaboration skills in cross-functional teams.

Nice To Haves

Experience working with big data processing frameworks (Spark, Flink, etc.).
Knowledge of real-time data pipelines and event-driven architectures.
Familiarity with security best practices in backend systems and infrastructure.
Prior experience in high-growth startups or ML-driven applications.

Responsibilities

Backend Development: Design, develop, and maintain scalable, high-performance platforms including services and APIs.
System & Infrastructure Contributions: Build and optimize infrastructure for ML model training and production, ensuring reliability and efficiency.
Database & Storage Optimization: Design and maintain efficient data pipelines, databases, and storage solutions for high-throughput applications.
Scalability & Performance Tuning: Ensure backend services are highly available, resilient, and performant, handling large-scale data workloads.
Collaboration: Work closely with ML engineers, data scientists, and DevOps teams to integrate models into production and enhance system architecture.
Security & Reliability: Implement best practices for security, monitoring, and fault tolerance to maintain system integrity.
Code Quality & Best Practices: Write clean, maintainable, and testable code, adhering to industry best practices.
Continuous Improvement: Evaluate and adopt new technologies and architectures to improve backend and ML system performance.