Machine Learning Engineer

About Oversight

Oversight is the world's leading provider of AI-based spend management and risk mitigation solutions for large enterprises. Based in Atlanta, GA, Oversight works with many of the world's most innovative companies and government agencies to digitally transform their spend audit and financial control processes.

Oversight's AI-powered platform works across our customers' financial systems to continuously monitor and analyze all spend transactions for fraud, waste, and misuse. With a consolidated, consistent view of risk across their enterprise, customers can prevent financial loss and optimize spend while strengthening the controls that improve compliance.

Position Overview: Job Purpose

We are seeking a highly skilled and experienced Machine Learning Engineer to join our team. The ideal candidate will have a proven track record of designing, building, and deploying machine learning models in production environments. You will be responsible for developing and optimizing machine learning systems that are integral to our products, using a robust modern stack.

Experience

• 3+ years of hands-on experience in machine learning and software engineering.
• Proven experience in building and deploying ML models in production environments at scale.
• Experience in a software company where machine learning is a core product component.

Responsibilities

• Design, develop, and deploy machine learning models that power critical product features.
• Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to integrate ML models into scalable production systems.
• Leverage Databricks to build and optimize scalable data pipelines that support machine learning workflows, ensuring efficient data processing, model training, and deployment in production environments.
• Optimize and maintain ML pipelines, including data ingestion, feature engineering, model training, and inference.
• Implement and manage the deployment of ML models on cloud platforms, particularly using AWS services.
• Conduct regular performance monitoring and improvement of models in production.
• Work with data engineers to ensure efficient data processing pipelines in Databricks and Snowflake.
• Stay current with advancements in machine learning technologies and frameworks, applying them to improve existing models and systems.
• Participate in code reviews and maintain high standards of engineering best practices in codebase and ML lifecycle management.
• Leverage Databricks to build and optimize scalable data pipelines that support machine learning workflows, ensuring efficient data processing, model training, and deployment in production environments.

Skills

• Proficiency in Python and its ML ecosystem (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong experience with cloud services (AWS), particularly for model deployment and data pipelines.
• Familiarity with data processing and analytics platforms such as Databricks and Snowflake.
• Solid understanding of software engineering best practices, including version control, CI/CD pipelines, and containerization (Docker, Kubernetes).
• Strong experience in working with structured and unstructured data.
• Experience with model monitoring, A/B testing, and retraining workflows in production.

Preferred Skills

• Experience in handling big data and real-time streaming data.
• Familiarity with MLOps practices and tools.
• Strong problem-solving skills and the ability to work autonomously.

Key Competencies

• Strong communication and collaboration skills.
• Ability to explain complex ML models and their implications to non-technical stakeholders.
• A proactive mindset and an eagerness to continually learn and apply new technologies and techniques.
• Ability to work in a fast-paced, dynamic environment and manage multiple priorities effectively.

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. Advanced degrees are strongly preferred.