Data Scientist / ML Engineer Gen AI

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:

Freddie Mac is seeking a hands-on Data Scientist / ML Engineer to lead the design, development, and deployment of innovative AI/ML, Generative AI (GenAI), and Agentic AI solutions for Enterprise Risk organization. This role requires advanced proficiency in data science, data engineering, and expertise in LLMs/GenAI to build scalable, production-ready systems that drive business impact. You will work across the entire AI lifecycle, from data collection and model experimentation to production deployment and monitoring, collaborating with cross-functional teams to deliver enterprise-grade AI solutions.

Key Responsibilities:

• End-to-End AI/ML Solution Delivery: Own the full lifecycle of AI/ML projects, including problem framing, data acquisition, feature engineering, model/LLM selection and fine-tuning, evaluation, deployment, monitoring, and continuous improvement.
• Data Engineering and Pipelines: Design, build, and maintain robust, scalable data pipelines for ingestion, preprocessing, and feature engineering, supporting both structured and unstructured enterprise data.
• LLM/GenAI & Agentic AI: Implement RAG pipelines using vector databases and embedding strategies to ground LLMs in proprietary enterprise data; fine-tune, prompt-engineer, and evaluate LLMs for domain-specific tasks; design and orchestrate Agentic workflows including tool-using agents, multi-step planners, guardrails, and alignment mechanisms.
• Trustworthy AI & Risk Controls: Establish robust evaluation frameworks (hallucination checks, calibration, bias/fairness, adversarial tests), logging/telemetry, safeguards, governance artifacts, and documentation for model risk management.
• MLOps and Deployment: Lead the end-to-end lifecycle of AI models, from experimentation and prototyping to scalable deployment in production environments using MLOps best practices, CI/CD, and cloud platforms (AWS, Azure, Google Cloud Platform).
• Performance Monitoring and Optimization: Optimize inference latency, throughput, and cost; establish monitoring and observability to ensure performance, safety, and reliability in mission-critical environments.
• Collaboration and Strategy: Work closely with AI engineers, software engineers, product managers, and business stakeholders to translate complex business problems into AI-native solutions with measurable impact.

Our Impact:

At Freddie Mac, we are at the forefront of technological innovation, developing AI solutions that transform complex business challenges into streamlined, automated processes. By leveraging advanced AI/ML, GenAI, and Agentic AI technologies, we enable businesses to enhance operational efficiency, make data-driven decisions, and unlock new opportunities for growth. Our commitment to trustworthy AI and robust risk controls ensures our solutions are reliable, safe, and compliant with industry standards, delivering impactful and sustainable results.

Your Impact:

As a Data Scientist / ML Engineer, you will play a pivotal role in shaping the future of AI-driven business solutions. Your expertise in data science, LLMs, and Agentic AI will directly influence how businesses operate and succeed. By collaborating with cross-functional teams, you will drive the implementation of scalable, production-ready AI solutions that meet the highest standards of performance, safety, and reliability.

Qualifications:

• Bachelor's or equivalent experience; advanced studies/degree preferred. Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field (or equivalent practical experience).
• 5+ years of experience in designing and deploying production-grade AI/ML solutions, including at least one production LLM agent or Agentic workflow.
• Deep expertise in Python and SQL, with 3+ years of experience in data science and AI/ML frameworks (scikit-learn, TensorFlow, PyTorch).
• 3+ years of proven experience with cloud-native development and data warehousing solutions (Snowflake, Azure Data Lake, AWS S3).
• Strong knowledge of LLMs, transformers, NLP, agentic modeling, and reinforcement learning concepts.
• 2+ years of experience with open and commercial LLMs, RAG pipelines, and agent frameworks (LangGraph/LangChain Agents, DSPy, or equivalent).
• Expertise in building data ETL/ELT pipelines and deploying models/LLMs using Docker/Kubernetes, CI/CD, and monitoring/observability tools.
• Excellent analytical, problem-solving, and critical-thinking skills.
• Demonstrated ability to work in cross-functional agile teams.

Keys to Success in this Role :

• Technical Proficiency: Demonstrate deep expertise in data science, LLMs, and Agentic AI. Your ability to design and implement scalable solutions will be essential.
• Quality and Compliance: Establish and enforce evaluation frameworks and procedures to ensure production-ready deployment of AI/ML solutions.
• Collaboration: Work effectively with AI engineers, software engineers, product managers, and business stakeholders to build comprehensive AI experiences. Strong communication skills and a team-oriented mindset are key.
• Innovation and Problem-Solving: Employ modern AI/ML and GenAI design patterns and best practices to solve complex business problems. Your innovative approach will help integrate advanced AI into application architectures successfully.
• Agility and Adaptability: Thrive in a fast-paced, agile environment. Your flexibility and ability to adapt to new challenges and technologies will ensure the continuous improvement of our AI solutions.

Current Freddie Mac employees please apply through the internal career site.

We consider all applicants for all positions without regard to gender, race, color, religion, national origin, age, marital status, veteran status, sexual orientation, gender identity/expression, physical and mental disability, pregnancy, ethnicity, genetic information or any other protected categories under applicable federal, state or local laws. We will ensure that individuals are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

A safe and secure environment is critical to Freddie Mac's business. This includes employee commitment to our acceptable use policy, applying a vigilance-first approach to work, supporting regulatory mandates, and using best practices to protect Freddie Mac from potential threats and risk. Employees exercise this responsibility by executing against policies and procedures and adhering to privacy & security obligations as required via training programs.

CA Applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

Notice to External Search Firms: Freddie Mac partners with BountyJobs for contingency search business through outside firms. Resumes received outside the BountyJobs system will be considered unsolicited and Freddie Mac will not be obligated to pay a placement fee. If interested in learning more, please visit and register with our referral code: MAC.

Time-type:Full time

FLSA Status:Exempt

Freddie Mac offers a comprehensive total rewards package to include competitive compensation and market-leading benefit programs. Information on these benefit programs is available on our Careers site.

This position has an annualized market-based salary range of $144,000 - $216,000 and is eligible to participate in the annual incentive program. The final salary offered will generally fall within this range and is dependent on various factors including but not limited to the responsibilities of the position, experience, skill set, internal pay equity and other relevant qualifications of the applicant.