Machine Learning Engineer

Schellman is a Top 50 CPA firm and a leading provider of attestation and compliance services. Our professional services focus on security and privacy audits, assessments, and certifications. Schellman has become one of the largest cybersecurity assessment firms in the United States without providing any traditional accounting services. We are an accredited multi-framework ISO Certification Body for security, privacy, business continuity, and quality; a globally licensed PCI Qualified Security Assessor and a top provider to clients serving the federal DoD space as a leading FedRAMP 3PAO and the first assessment firm authorized as a CMMC C3PAO. Our specialty and expertise remain in providing best in class Cybersecurity and IT Audits and Attestations. Our culture, approach with clients, and dedication to our values has led us to consistently be a Great Places to Work certified company and rated as a Best Firms to Work For by Accounting Today and a Glassdoor Best Places to Work. We deeply appreciate our employees, as shown by our first core value – People Come First. This is demonstrated in our culture, benefits, and how we handle business. Come see what makes Schellman special!Job SummaryThe Machine Learning Engineer at Schellman is a critical role responsible for developing and deploying machine learning models, with a specific focus on Large Language Models (LLMs). This position involves both research and practical application, requiring a deep understanding of ML techniques, data science principles, and the ability to implement solutions that drive product innovation. The Machine Learning Engineer will collaborate closely with cross-functional teams, including Data Engineers, Product Managers, and Software Engineers, to integrate LLMs into our products. This role will report to the Sr. Director of Product Management and Strategy.Essential Functions• Model Tuning and Training: Tune pre-existing Large Language Models (LLMs) to address specific business challenges, enhance user experiences, and improve operational efficiencies. Ensure that the models are effectively trained to meet our product and service goals.• Data Preprocessing: Prepare and preprocess data for training, ensuring high-quality input for model accuracy and performance.• Model Deployment: Implement and deploy LLMs in production environments, ensuring scalability, reliability, and maintainability.• Collaboration: Work closely with Data Engineers, Product Managers, and Software Engineers to align ML solutions with product goals and technical requirements.• Research and Innovation: Stay updated with the latest advancements in machine learning, particularly in LLMs, and apply this knowledge to improve our AI capabilities.• Performance Monitoring: Monitor and evaluate model performance, using metrics to track accuracy, efficiency, and effectiveness, iterating as needed.• Documentation: Document processes, methodologies, and results to ensure knowledge transfer and reproducibility.• Optimization: Continuously optimize LLMs for performance and cost-efficiency, leveraging techniques such as model compression and parallel processing.• Technical Support: Provide technical support and expertise to other teams within the organization on matters related to ML and LLMs.Knowledge, Skills, And Abilities• Proficiency in Machine Learning Frameworks with strong knowledge of TensorFlow, PyTorch, and Hugging Face Transformers.• Deep understanding of Large Language Models (LLMs) with expertise in their applications to solve real-world problems.• Proficiency in Programming Skills with expertise in Python.• Data Processing Expertise in data preprocessing, feature engineering, and model evaluation to ensure high model performance.• Analytical and Problem-Solving Skills demonstrated by the ability to analyze large datasets and derive actionable insights.• Strong Communication and Collaboration Skills, capable of working effectively with cross-functional teams.• Knowledge in Cloud and ML Operations, familiar with AWS services including Bedrock and MLOps practices.• Commitment to continuous learning, staying up-to-date with the latest advancements in AI and machine learning.Education, Work Experience, And Certifications• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, related field, or equivalent experience.• 3+ years of experience in machine learning engineering, with a focus on LLMs and NLP.• Experience with version control systems (e.g., Git), continuous integration, and continuous deployment (CI/CD) pipelines.• Prior experience in deploying and maintaining ML models in production environments.• Relevant certifications in machine learning and AI (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning) are a plus.Schellman is an equal opportunity employer (EOE) and strongly supports diversity in the workplace; therefore, providing equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law. Schellman uses E-Verify in our hiring process.At Schellman, we strive to provide a flexible and balanced environment and therefore offer the opportunity to work remotely, unless otherwise stated in the job requirements. Connecting, collaborating and continuous education are also highly valued and therefore we require some travel annually for our Internal Service Delivery roles, which can include in-person training, team meet-ups, and strategy meetings. Service Delivery team members will also be required to travel based on business and client needs.