Machine Learning Engineer II

PROS • Houston, TX

3d

About The Position

PROS is seeking a Machine Learning Engineer II to build, deploy, and operate scalable machine learning solutions within the PROS Platform. This role focuses on productionizing ML models, optimizing performance at a scale, and owning well-defined ML components while collaborating closely with data scientists and software engineers. Design, implement, and productionize machine learning models and data pipelines in collaboration with data scientists and engineers. Convert research and prototype workflows into scalable, reliable, and secure production systems. Build and optimize distributed ML pipelines for large-scale training and low-latency inference. Apply ML best practices for feature engineering, model tuning, validation, and performance optimization. Deploy, monitor, and maintain ML systems in production; diagnose and resolve performance and reliability issues. Evaluate existing ML pipelines and recommend improvements to architecture, tooling, and processes. Extend and optimize shared ML libraries and frameworks to support reuse and consistency. Partner with software engineers to integrate ML solutions into the platform and meet SLA requirements.

Requirements

• 5+ years of progressively responsible experience (including time spent to pursue advanced degree) in machine learning engineering or data-intensive software engineering.
• Strong proficiency in Python and experience building production-grade ML systems.
• Hands-on experience with distributed data and ML frameworks such as PySpark, Databricks, and MLflow.
• Experience with deep learning frameworks (TensorFlow and/or PyTorch).
• Strong understanding of distributed systems, performance tuning, and cost optimization.
• Experience deploying, monitoring, and maintaining ML models for batch and real-time inference.
• Familiarity with Linux environments and cloud platforms, preferably Microsoft Azure.
• Strong communication skills and ability to work independently on well-defined problems.

Nice To Haves

• Advanced degree in Computer Science, Machine Learning, Data Science, or a related field.
• Experience with GPU-accelerated training or inference.
• Exposure to advanced ML techniques and large-scale optimization problems.
• Experience improving shared ML platforms, tooling, or libraries used across teams.
• Understand core AI concepts and apply them ethically to enhance productivity, insights, and decision-making.
• Craft effective prompts to optimize the quality and relevance of AI-generated outputs.
• Explore and apply agentic AI systems, using or managing autonomous agents to streamline workflows and automate tasks.
• Leverage AI tools to boost efficiency, creativity, and innovation in their daily work.
• Stay curious and adaptable, continuously experimenting with AI-driven solutions to elevate team performance and customer impact.

Responsibilities

• Design, implement, and productionize machine learning models and data pipelines in collaboration with data scientists and engineers.
• Convert research and prototype workflows into scalable, reliable, and secure production systems.
• Build and optimize distributed ML pipelines for large-scale training and low-latency inference.
• Apply ML best practices for feature engineering, model tuning, validation, and performance optimization.
• Deploy, monitor, and maintain ML systems in production; diagnose and resolve performance and reliability issues.
• Evaluate existing ML pipelines and recommend improvements to architecture, tooling, and processes.
• Extend and optimize shared ML libraries and frameworks to support reuse and consistency.
• Partner with software engineers to integrate ML solutions into the platform and meet SLA requirements.

Benefits

• flexible ways of working
• continuous learning

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level