Agile Development Senior (Software Engineer, Single-Family Machine Learning Enablement)

Freddie Mac•McLean, VA

1d•$127,000 - $191,000

About The Position

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. Position Overview: Are you someone who thrives in a highly visible, fast-paced data science environment? Do you have a deep understanding and passion for excellence in technology, especially in supporting data-driven solutions and meeting delivery timelines? If you have proven experience, the drive, and know what it takes to help teams successfully implement innovative, fast-to-market data science products, we are your next destination employer! Apply to join Freddie Mac’s Single-Family Acquisitions AI/ML Enablement team. We are seeking a talented software developer or machine learning engineer with expertise in modern delivery practices, distributed data processing, and advanced machine learning frameworks—including generative AI. In this role, you will support data scientists in running machine learning experiments, developing and deploying models, and leveraging generative AI to solve complex business challenges within the Single-Family division. Our Impact: Freddie Mac’s Single-Family Line of Business is in the throes of a major digital modernization and transformation. We are changing the way we work every day. Provide efficient software and data-driven solutions for business requirements. Assist data scientists in running machine learning experiments and optimizing their workflow. Help support teams to analyze and resolve business raised incidents, including those related to data and machine learning models. Your Impact: You will be a key executor to the team, as a machine learning engineer, you will be assisting data scientists in their research and development work in building data pipelines, developing and deploying machine learning models, and taking on responsibilities across the entire data and ML stack. You will be contributing across several other technical activities such as data engineering, model development, deployment, MLOps, and quality of the product while working closely with the Technical Lead to analyze and come up with efficient solutions for software and data challenges related to R&D work by Single-Family data scientists.

Requirements

Bachelor’s degree in information technology, Computer Science, Engineering, Data Science, or related field, or equivalent combination of education and work experience.
A minimum of 5-7 years of experience engineering robust machine learning solutions.
5+ years experience in Machine Learning processes: data preparation, exploratory data analysis, model training and evaluation, linear algebra, statistical concepts, algorithm design, software engineering practices.
Knowledge / hands-on experience with multiple technologies such as: Apache Spark, Ray Project, PyTorch, scikit-learn, NumPy, and pandas.
Strong proficiency in Python and working with the Python ecosystem.
Knowledgeable about various Python package building and distribution methods (e.g., setuptools, pip, conda).
Comfortable working with open-source packages and ability to debug issues with open-source.
Must have strong prior hands-on development experience with distributed data processing (Apache Spark, Ray), deep learning frameworks (PyTorch), and machine learning libraries (scikit-learn).
2+ years familiar with the use of AI-assisted coding tools, such as Github Copilot, Claude Code, Amazon Q, Cursor, etc.
Must be familiar with high pace agile environment and active participation in agile ceremonies like standups, refinement sessions, retrospectives and reviews is required.
Large-scale data architectures, container orchestration frameworks, and deployment of machine learning models in production.
Proven understanding of developing end-to-end machine learning workflows, including data preparation, model training, hyperparameter evaluation, and model serving.
Demonstrate strong interpersonal and communication skills by actively engaging with team members, stakeholders, and cross-functional partners.
Effectively share ideas, technical concepts, and project updates both verbally and in writing, ensuring clarity and understanding.
Embrace open dialogue and transparency to build trust and drive team success.
Ability to work effectively and productively in a team or independently.
Technology-first approach, outstanding analytical skills and drive for new technologies and tools.
Innovative in providing solutions, like to take on challenges with calculated risk - fail fast and learn fast.
Employees exercise this responsibility by executing against policies and procedures and adhering to privacy & security obligations as required via training programs.

Nice To Haves

Advanced studies/degree preferred.
Experience with E2E testing tools and model validation techniques is a huge plus.
2+ years with data storage solutions such as PostgreSQL, MongoDB, Snowflake, and cloud platforms is highly preferred.
Information and application security including firewall, AD, TAM/PING, LDAP, certificates, public key encryption, SSH, access credentials, etc.

Responsibilities

Provide efficient software and data-driven solutions for business requirements.
Assist data scientists in running machine learning experiments and optimizing their workflow.
Help support teams to analyze and resolve business raised incidents, including those related to data and machine learning models.
Assisting data scientists in their research and development work in building data pipelines, developing and deploying machine learning models, and taking on responsibilities across the entire data and ML stack.
Contributing across several other technical activities such as data engineering, model development, deployment, MLOps, and quality of the product while working closely with the Technical Lead to analyze and come up with efficient solutions for software and data challenges related to R&D work by Single-Family data scientists.