Senior Machine Learning Engineer (Databricks)

Steampunk•McLean, VA

About The Position

We are looking for a seasoned Senior Machine Learning Engineer to work with our existing team of Data Scientists and Engineers to apply AI/ML technologies in support of Federal use cases, with a focus on solutions built within the Databricks (DBX) platform. We are seeking a technologist with excellent communication and customer service skills, and a passion for data-driven problem solving. Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. As an employee owned company, we focus on investing in our employees to enable them to do the greatest work of their careers – and rewarding them for outstanding contributions to our growth. If you want to learn more about our story, visit http://www.steampunk.com.

Requirements

Ability to hold a position of public trust with the U.S. government.
Bachelor’s degree in computer science, data science, statistics, information systems, engineering, business, or a related scientific or technical discipline.
5–7 years of industry experience developing ML/AI solutions and solving complex problems.
5–7 years of experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.
Strong programming skills in languages such as Python, R, or Java.
Solid understanding of statistical methods, data structures, and algorithms.
Experience with data preprocessing, feature engineering, and model evaluation techniques.
Experience working with distributed data processing frameworks such as Apache Spark.
Hands-on experience developing and deploying machine learning solutions within Databricks (DBX) environments.
Experience developing and deploying machine learning workflows within Databricks, including use of MLflow or similar model lifecycle tools.
Knowledge of cloud platforms such as AWS, Google Cloud, or Azure.
Experience with version control systems (e.g., Git) and CI/CD practices.
Excellent problem-solving skills and attention to detail.
Strong communication skills and ability to work collaboratively in a team environment.
Ability to manage multiple tasks and projects simultaneously while meeting deadlines.

Responsibilities

Collaborate with cross-functional teams to design and develop machine learning models and algorithms to address business needs.
Analyze and process large-scale datasets using distributed data processing frameworks (e.g., Spark) to extract meaningful insights and patterns.
Collaborate with cross-functional teams to integrate machine learning solutions into existing systems and workflows.
Optimize and fine-tune machine learning models for performance, scalability, and accuracy.
Implement and maintain data pipelines to support machine learning workflows within Databricks environments.
Utilize Databricks capabilities (e.g., notebooks, jobs, workflows, MLflow) to develop, track, and manage machine learning experiments and model lifecycle.
Evaluate the effectiveness of machine learning models using appropriate metrics and validation techniques.
Document processes, experiments, and results to ensure reproducibility and knowledge sharing.
Deploy machine learning models to production environments and monitor their performance.
Contribute to the growth of our AI & Data Exploitation Practice.