Data Scientist
Join AI Squared as a Data Scientist to develop AI/ML solutions and collaborate with product and engineering teams in a hybrid role.
Posted by employer 11 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
You will collaborate closely with product and engineering teams, applying state-of-the-art techniques to solve complex challenges.
Requirements
- Experience
- 5+ years
Not disclosed in this posting: compensation, visa sponsorship.
Joblaze summary
In this role, the Data Scientist will focus on developing and optimizing machine learning models and pipelines, particularly leveraging large language models for various applications. The position requires expertise in Python, ML frameworks like PyTorch or TensorFlow, and familiarity with containerization tools such as Docker and Kubernetes, along with experience in cloud platforms. This opportunity is ideal for seasoned professionals with over five years in data science or machine learning, who thrive in collaborative environments and are passionate about translating research into practical AI solutions.
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from AI Squared's career page.
- AI Squared has 7 other open roles including QA Engineer, Account Executive - Enterprise, Sales Engineer.
- 6527 active Python roles on Joblaze right now.
- 2727 active AWS roles on Joblaze right now.
- 2568 active Kubernetes roles on Joblaze right now.
- Typical Python salary we track runs $125,000–$190,800 (median $160,000, across 3229 roles). See Python salary trends
Quick facts
- Is the Data Scientist role remote?
- It's hybrid — AI Squared expects some on-site time in Washington, DC.
- How much experience is required?
- At least 5 years of relevant experience for this Data Scientist role.
- Where is the role based?
- AI Squared is hiring for this position in Washington, DC.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AWS, Azure, Databricks, Docker, GCP, Kubernetes.
- What seniority level is this role?
- AI Squared targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Data Scientist role at AI Squared.
From the original posting
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.
- 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.