Senior Software Engineer, AI/ML Platform

Job Description

Senior Software Engineer, AI/ML Platform | [Your Company Name]

The Tone:
This is a Senior Software Engineer, AI/ML Platform role at [Your Company Name], located in San Francisco, CA, with hybrid remote flexibility. [Your Company Name] is a pioneering technology company that creates transformative solutions to empower businesses and individuals worldwide. This pivotal role is instrumental in designing, building, and maintaining robust, scalable, and efficient infrastructure that enables our data scientists and ML engineers to develop, deploy, and monitor cutting-edge machine learning models. You will directly contribute to the intelligence behind our core products.

The TL;DR
• Role: Full Time
• Location: San Francisco, CA (Hybrid Remote)

• Team: Engineering / AI/ML Platform team, reporting to the Director of Engineering, AI/ML
• Mission: This role designs, builds, and maintains robust, scalable, and efficient infrastructure and tools, empowering data scientists and ML engineers to develop, deploy, and monitor machine learning models across our product suite.
• Tech Stack: Python, Java, Go, Scala, AWS, GCP, Azure, Kubernetes, S3, GCS, EC2, GCE, Lambda, Cloud Functions, Spark, Flink, Kafka, TensorFlow, PyTorch, Scikit-learn, Docker, MLflow, Kubeflow, Sagemaker, Vertex AI

What You’ll Actually Do
• Design: Lead the design and implementation of core components for our AI/ML platform, including data pipelines, training frameworks, inference services, and monitoring tools.
• Build: Create highly scalable, fault-tolerant, and performant systems capable of handling large datasets and high-volume ML inference requests.
• Collaborate: Partner closely with data scientists, ML engineers, product managers, and other engineering teams to translate their needs into platform features and services.
• Advocate: Champion and implement best practices in software engineering, MLOps, CI/CD, testing, and system reliability.
• Mentor: Provide technical leadership and mentorship to junior engineers, fostering a culture of excellence and continuous improvement.

The Must-Haves
• Background: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field, with a focus on building scalable backend systems or AI/ML infrastructure.
• Experience: 5+ years of professional software development experience, including at least 3 years specifically building scalable backend systems or AI/ML infrastructure.
• Skills: Strong proficiency in Python, Java, Go, or Scala; hands-on experience designing and building distributed systems, microservices, and RESTful APIs; expertise with major cloud providers (AWS, GCP, Azure) and their managed services; experience with big data technologies such as Spark, Flink, and Kafka; solid understanding of the machine learning lifecycle and MLOps principles.
• Bonus: Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn; familiarity with containerization and orchestration technologies like Docker and Kubernetes; experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, Sagemaker, Vertex AI); background in data engineering or building large-scale data pipelines; contributions to open-source projects.