About the job
About Sciemo
Sciemo is at the forefront of AI innovation for consumer goods, providing technology that empowers businesses to make more informed, rapid, and human-centric decisions throughout the Integrated Business Planning (IBP) process. Our platform effectively transforms disorganized, isolated data into significant business outcomes. Through real-time assistance from our AI agents, we simplify complexity, enabling decision-makers to focus on what truly matters. Our headquarters is located in New York City.
Overview
As a leading startup, Sciemo is dedicated to advancing AI solutions for consumer brands. Utilizing machine learning, generative AI, agent-based systems, and graph technologies, we enable our clients to extract insights within seconds and achieve business impacts in minutes through our innovative products.
We are seeking a Founding Member of Technical Staff to join our team, overseeing the development and deployment of sophisticated ML systems, reporting directly to Dan Wald, our Co-Founder & Chief AI Officer.
Role
In this dual capacity as a Data Scientist and Machine Learning Engineer, you will be instrumental in crafting, developing, and deploying the intelligent solutions that power our AI products. Your expertise will span the full spectrum of applied AI, including data science, machine learning, and large-scale production engineering. This role necessitates a combination of innovative model development and the engineering aptitude to deploy and sustain these models within robust, scalable infrastructures.
You will collaborate closely with data engineers, product leaders, backend developers, and customer-facing teams to ensure our AI systems yield tangible value in real-world applications. As an early technical hire, you will help shape our AI strategy, establish technical standards, and create best practices for scalable applied AI.
Responsibilities
Develop and deploy AI systems that include:
Architecting, building, and deploying ML/GenAI products on cloud infrastructure (AWS or equivalent).
Designing and implementing end-to-end AI workflows: data ingestion, feature engineering, modeling, evaluation, and deployment.
Creating automated pipelines for continuous learning, model promotion, and performance monitoring.
System architecture and reliability:
Leading the design of ML orchestration frameworks (such as Airflow, Kedro, ZenML, Flyte) to ensure reproducibility and reliability.

