Job description

Data Scientist Washington, DC (Hybrid) About the Role: We are looking for a highly motivated Data Scientist with a strong background in applied machine learning and AI to join our growing team. In this role, you will be a key contributor to the development of core AI/ML solutions that power our platform. You will collaborate closely with product and engineering teams, applying state-of-the-art techniques to solve complex challenges, advance our use of large language models (LLMs), and ensure scalable, production-ready solutions. Key Responsibilities: Leverage 5+ years of experience in data science to design, implement, and optimize machine learning models and pipelines. Develop, fine-tune, and evaluate large language models (LLMs) for a variety of applications, ensuring accuracy, performance, and robustness. Collaborate with engineering and product teams to integrate AI/ML solutions into our platform in a scalable and maintainable way. Conduct applied research, staying current on advances in LLMs, generative AI, and data science methodologies, and translate them into practical solutions. Build end-to-end workflows, from data exploration and feature engineering to training, validation, deployment, and monitoring in production. Apply modern containerization and orchestration techniques (e.g., Docker, Kubernetes) to support reproducible experimentation and deployment. Work with cloud platforms (e.g., Databricks, AWS, GCP, Azure) to manage data pipelines, large-scale training jobs, and distributed systems. Collaborate across teams to ensure our AI capabilities align with platform goals and business needs. Qualifications: 5+ years of experience as a Data Scientist or Machine Learning Engineer, with proven success in deploying models to production. Hands-on experience with large language models (LLMs); fine-tuning experience strongly preferred. Strong background in Python and ML frameworks such as PyTorch or TensorFlow. Proficiency in containerization and orchestration technologies (Docker, Kubernetes). Experience with cloud platforms and ML ecosystems (Databricks, AWS, GCP, Azure). Familiarity with MLOps best practices, including model deployment, monitoring, and CI/CD for ML. Strong analytical and problem-solving skills, with the ability to translate research into production-ready solutions. Excellent communication and collaboration skills, with the ability to work effectively across product, engineering, and leadership teams. A proactive, self-starter mindset with a passion for applied research and innovation.

Requirements

Must-haves

Data Scientist
Washington, DC (Hybrid)
Leverage 5+ years of experience in data science to design, implement, and optimize machine learning models and pipelines.
Develop, fine-tune, and evaluate large language models (LLMs) for a variety of applications, ensuring accuracy, performance, and robustness.
Collaborate with engineering and product teams to integrate AI/ML solutions into our platform in a scalable and maintainable way.
Conduct applied research, staying current on advances in LLMs, generative AI, and data science methodologies, and translate them into practical solutions.
Build end-to-end workflows, from data exploration and feature engineering to training, validation, deployment, and monitoring in production.
Apply modern containerization and orchestration techniques (e.g., Docker, Kubernetes) to support reproducible experimentation and deployment.
Work with cloud platforms (e.g., Databricks, AWS, GCP, Azure) to manage data pipelines, large-scale training jobs, and distributed systems.
Collaborate across teams to ensure our AI capabilities align with platform goals and business needs.
5+ years of experience as a Data Scientist or Machine Learning Engineer, with proven success in deploying models to production.
Strong background in Python and ML frameworks such as PyTorch or TensorFlow.
Proficiency in containerization and orchestration technologies (Docker, Kubernetes).
Experience with cloud platforms and ML ecosystems (Databricks, AWS, GCP, Azure).
Familiarity with MLOps best practices, including model deployment, monitoring, and CI/CD for ML.
Strong analytical and problem-solving skills, with the ability to translate research into production-ready solutions.
Excellent communication and collaboration skills, with the ability to work effectively across product, engineering, and leadership teams.
A proactive, self-starter mindset with a passion for applied research and innovation.

Nice-to-haves

Hands-on experience with large language models (LLMs); fine-tuning experience strongly preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimldockerkubernetesawsgcpazurepythonci/cd

Platform: greenhouse
Effort: quick