Data Science & Machine Learning Engineer - Austin, TX OR Raleigh, NC - Hybrid
Role details
Job location
Tech stack
Job description
We have an immediate need for a Data Science & Machine Learning Engineer for an onsite hybrid opportunity in Austin, TX 78753 OR Raleigh, NC. This role requires candidates to work from the office 3 days a week and offers the opportunity to support high-impact AI and analytics initiatives for enterprise-scale environments. The engagement involves working closely with cross-functional product, analytics, and engineering teams to drive innovation across data science, machine learning, analytics engineering, and Agentic AI solutions.
Requirements
The ideal candidate will be responsible for understanding stakeholder data needs, translating business requirements into scalable technical solutions, and building reliable data pipelines and analytical systems. The role involves designing and managing ETL workflows, transforming raw datasets into canonical models using dbt, and building dashboards and self-service analytics tools for business teams. Candidates will work extensively with SQL, Python, Airflow, GitHub, and visualization platforms while contributing to AI-focused initiatives including prompt engineering, evaluation frameworks, model quality assurance, and foundational AI infrastructure improvements. The selected professional will collaborate with product, research, and safeguards teams to support model launches, monitor regressions, optimize prompts, and enhance AI system performance. This position requires strong problem-solving ability, adaptability in fast-paced environments, and a full-stack mindset to solve complex end-to-end challenges across analytics and AI ecosystems. Required skill sets include strong expertise in Agentic AI, Machine Learning, Data Science, SQL, Python, ETL development, dbt, Airflow, and GitHub. Candidates should possess experience building scalable analytics engineering solutions, data models, dashboards, and AI-driven workflows. Strong understanding of LLMs, prompt engineering, AI evaluation methodologies, experimentation frameworks, CI/CD practices, and data architecture concepts is highly preferred. Experience with data visualization tools, machine learning infrastructure, NLP concepts, AI safety considerations, and analytics reporting environments will be an added advantage. Candidates should have excellent communication skills and the ability to collaborate across technical and business teams in rapidly evolving AI-driven environments. Candidates who can directly work on our payroll are encouraged to apply.