Senior Software Engineer

This is a fully remote position, open to applicants in Texas.

📋 Description

• Oversee the design and advancement of Pearson's Kubernetes-driven machine learning platform that facilitates large-scale model training and deployment.

• Create, implement, and enhance distributed machine learning workflows utilizing MetaFlow and various cloud-native technologies.

• Develop platform functionalities for reproducible experimentation, automated model training, artifact management, and deployment in production.

• Build infrastructure that supports GPU-based workloads for conventional machine learning models, foundational models, and agentic pipelines.

• Design and execute backend services and APIs that facilitate machine learning lifecycle management.

• Assess and incorporate open-source technologies to enhance developer productivity, platform reliability, scalability, and operational efficiency.

• Work alongside AI scientists to transform research prototypes into resilient, scalable, production-grade systems.

• Enhance platform observability, reliability, security, and efficiency in cloud cost management.

• Guide engineers, contribute to the technical strategy, and establish engineering best practices throughout the team.

⛳️ Requirements

• A Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent professional experience.

• Significant software engineering experience in developing complex distributed systems.

• Proficient in Python development at an expert level.

• Experience in designing and implementing cloud-native applications on AWS.

• Familiarity with developing applications utilizing Kubernetes and container technologies.

• Experience in designing RESTful APIs and microservice architectures.

• Proficient in working with both SQL and NoSQL databases.

• Familiarity with CI/CD pipelines, Git-based development workflows, and automated testing.

• Strong problem-solving, communication, and collaboration abilities.

• Preferred: Experience with machine learning platforms such as MetaFlow, MLflow, Kubeflow, or comparable workflow orchestration systems.

• Preferred: Background in production machine learning systems.

• Preferred: Experience with GPU computing and distributed model training.

• Preferred: Knowledge of large language model deployment or inference infrastructure.

• Preferred: Familiarity with PyTorch, TensorFlow, or similar machine learning frameworks.

• Preferred: Experience with Kubernetes operations, scheduling, and workload optimization.

• Preferred: Proficiency in Go development.

• Preferred: Familiarity with Infrastructure as Code technologies.

• Preferred: Skills in performance optimization and cloud cost management.

• Preferred: Experience in building internal developer platforms or engineering productivity tools.

• Experience creating platforms utilized by machine learning engineers and data scientists.

• Experience in deploying and managing production AI or LLM infrastructure.

• Experience in fine-tuning, deploying, and overseeing foundation models and pipelines.

• Experience in designing highly scalable cloud-native systems that manage large datasets and compute-intensive tasks.

• A curiosity about emerging AI technologies and the ability to evaluate them in a pragmatic manner.

• A passion for developing tools that empower others to accelerate their work.

🏝️ Benefits

• No eligibility for bonuses.

• Information regarding benefits can be found on the linked benefits page.