Data Scientist

AI Squared; Washington (Washington County); Hybrid; USD 120,000 - 190,000; Full time

Job summary

AI Squared in Washington, DC, is seeking a Data Scientist with a strong applied ML background to help develop core AI/ML solutions powering our platform. You will collaborate with product and engineering teams to design, prototype, and deploy scalable AI capabilities that enhance our product and user experience.

The role emphasizes hands-on model development, LLM refinement, and production-ready workflows, with a focus on reliability, performance, and maintainability across cloud and

Qualifications

• 5+ years of experience as a Data Scientist or ML Engineer with production deployments.
• Hands-on experience with large language models (LLMs); fine-tuning experience preferred.
• Strong Python background and ML frameworks such as PyTorch or TensorFlow.
• Proficiency with containerization and orchestration technologies (Docker, Kubernetes).
• Experience with cloud platforms and ML ecosystems (Databricks, AWS, GCP, Azure).
• Familiarity with MLOps practices including deployment, monitoring, and CI/CD for ML.
• Strong analytical and problem-solving skills; able to translate research into production-ready solutions.
• Excellent communication and collaboration with product, engineering, and leadership teams.

Responsibilities

• Design, implement, and optimize ML models and pipelines.
• Develop, fine-tune, and evaluate LLMs for various applications.
• Collaborate with engineering and product teams to integrate ML into the platform.
• Conduct applied research on LLMs, generative AI, and data science methods.
• Build end-to-end workflows from data exploration to training, validation, deployment, and monitoring.
• Apply Docker and Kubernetes to support reproducible experimentation and deployment.
• Work with cloud platforms to manage data pipelines and large-scale training jobs.
• Collaborate across teams to align AI capabilities with platform goals.

Skills

Data Scientist

LLMs

Python

PyTorch

TensorFlow

Docker

Kubernetes

Cloud platforms

MLOps

CI/CD

Communication

Collaboration

Tools

Databricks

AWS

GCP

Azure

Job description

Overview

Data Scientist – Washington, DC (Hybrid)

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.

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.