Senior Machine Learning Engineer

Summary

Join GoodLeap as a Senior Machine Learning Engineer and collaborate with engineering tech leads to deploy LLM models into production, build scalable ML infrastructure, and optimize ML workflows. You will play a crucial role in defining and scaling ML/AI applications, ensuring efficiency and reliability. Responsibilities include integrating LLM models, building scalable ML platforms, designing data pipelines, automating workflows, and implementing ML Ops best practices. You will also partner with cross-functional teams and optimize distributed processing architectures. This role requires a Master's degree in a related field with 5+ years of experience and strong programming skills in Java, Python, and SQL/MySQL. A competitive salary and potential bonus are offered.

Requirements

• Master’s degree in computer science, Machine Learning, or a related field with 5+ years of experience as an ML Engineer or ML Scientist in an industry setting
• Strong programming skills in Java, Python, and SQL/MySQL
• Hands-on experience in ML Ops, including large-scale ML applications, services, pipelines, and architectures
• Solid understanding of system design for ML systems, including design patterns, OOD (Object-Oriented Design), and interface design
• Experience with distributed processing architectures and ML/data workflow management platforms (e.g., Spark, Databricks, Airflow, Kubeflow, MLflow)
• Experience with containerization and orchestration tools like Docker and Kubernetes

Responsibilities

• Collaborate with engineering tech leads to integrate LLM models into production systems
• Identify and solve engineering pain points by building scalable, general-use ML platforms
• Design and develop scalable infrastructure and pipelines for data/feature processing, model training, and evaluation
• Automate ML workflows to improve productivity across training, evaluation, testing, and results generation
• Partner with cross-functional teams to define the long-term vision for ML/AI applications and contribute to roadmap planning
• Implement ML Ops best practices, ensuring efficient model deployment, monitoring, and versioning
• Optimize and manage distributed processing architectures using Spark, Databricks, Airflow, Kubeflow, MLflow, etc
• Develop microservices-based architectures for ML applications, including RESTful APIs for model serving
• Ensure compliance with scalability, reliability, and security standards in ML production systems

Preferred Qualifications

• Ph.D. in Computer Science, Machine Learning, or a related field, or 3+ years of ML Engineering experience in addition to a Master’s degree
• Strong theoretical and practical understanding of machine learning models and frameworks (Scikit-Learn, TensorFlow, PyTorch, etc.)
• Experience working with cloud-based solutions, especially AWS and Databricks
• Experience with CI/CD pipelines, automated testing, and test-driven development for ML applications
• Knowledge of microservice architectures and best practices for RESTful web services

Benefits

• $137,000 - $165,000 a year
• Bonus