Senior ML Engineer: Scalable Production Pipelines & GenAI

Richard, Wayne & Roberts; Arlington (VA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Relocation assistance

Direct hire with benefits

Job summary

Richard, Wayne & Roberts is seeking a Machine Learning Engineer to design, build, and productionize analytics across machine learning, deep learning, and generative AI. This role focuses on scalable pipelines and deploying models in production using Databricks and PySpark in Arlington, VA.

You will partner with stakeholders, optimize models, and ensure reliable, scalable ML systems with strong CI/CD practices.

Qualifications

• 10+ years of experience in machine learning engineering, data science, or software engineering.
• Strong programming skills in Python, SQL, or Scala.
• Experience with Databricks, PySpark, and cloud platforms (AWS, Azure, etc.).
• Hands-on experience with feature engineering, model optimization, and scalable data pipelines.

Responsibilities

• Partner with business stakeholders to define and deliver ML/AI solutions.
• Build, scale, and optimize model training and serving pipelines.
• Improve model performance through feature engineering, tuning, and observability.
• Deploy and integrate ML models into production with a focus on reliability and scalability.
• Collaborate with data engineers and data scientists to translate business needs into technical solutions.
• Drive best practices in architecture, code quality, and CI/CD pipelines.

Skills

Python

SQL

Scala

Databricks

PySpark

AWS

Azure

Education

Bachelor's degree in a quantitative field

Master's degree preferred

Job description

Richard, Wayne & Roberts is seeking a Machine Learning Engineer to design, build, and productionize analytics across machine learning, deep learning, and generative AI. This role focuses on scalable pipelines and deploying models in production using Databricks and PySpark in Arlington, VA.

You will partner with stakeholders, optimize models, and ensure reliable, scalable ML systems with strong CI/CD practices.