Senior Machine Learning Engineer

Overview

Job Title: Senior Machine Learning Engineer
Location: Los Angeles, CA (Remote allowed)
Salary: $125.40K - $165.30K/yr (Estimated pay)
Type: Full-time
Posted: 5 hours ago

As a Senior Machine Learning Engineer, you will build, deploy, and optimize the machine learning services that power the client s platform. You will be the primary builder of robust ML subsystems, translating high-level requirements into production-ready code. This is a hands-on role where you will focus on the reliability, speed, and scalability of our AI solutions. You will take ownership of specific model pipelines, ensuring they are efficient, testable, and maintainable.

• Write high-quality, production-grade software for data ingestion, feature extraction, and model inference.
• Implement robust CI/CD pipelines, automated testing, and comprehensive logging/monitoring.
• Construct and maintain data pipelines required for training and inference.
• Develop reusable software modules and utilities.
• Translate business requirements into technical specifications.
• Monitor the daily performance of production models, debug incidents, and execute routine retraining workflows.
• Partner with Engineering team members and Product Managers to estimate effort, flag technical risks, and deliver features on schedule.

• 5+ years of professional software development experience
• 3+ years of hands-on experience in machine learning engineering or applied AI
• Technical subject matter expertise in 3+ general areas of software development (e.g., server, database, security, etc) including machine learning infrastructure.
• Demonstrated ability to deliver significant, measurable real-world impact through applied ML.
• Proven ability to design and write modular, performant, and easy to read software that solves complex business problems.
• Proficiency in Python, Tensor Flow/PyTorch, and scikit-learn.
• Strong background in MLOps and data infrastructure (e.g., Airflow, Spark, feature stores, MLflow, data versioning).
• Proven ability to deploy and maintain ML models in production with CI/CD, monitoring, and alerting.
• Familiarity with cloud ML environments (AWS, GCP, or Azure) and containerization (Kubernetes, Docker).
• Experience building or fine-tuning Large Language Models (LLMs) or generative models for structured business processes.
• Experience with retrieval-augmented pipelines or feedback-driven model retraining.
• Excellent technical communication and a product mindset

• Background in healthcare software operations, or financial automation.
• Contributions to open-source ML infrastructure projects.
• Published research or conference papers in machine learning, natural language processing, or applied AI
• Experience leading AI reliability and observability initiatives

• Training & Development
• Performance Bonus