Member of Technical Staff

Sciemo•New York City, NY

2d•Hybrid

About The Position

Sciemo builds AI for consumer goods: technology that helps businesses make faster, smarter, and more human decisions across the entire Sales and Operations Planning (S&OP) cycle. From optimizing promotions to balancing demand and supply, Sciemo's platform transforms messy, siloed data into measurable business impact. Its AI agents assist decision-makers in real-time, turning complexity into clarity. We are headquartered in New York City. We are an industry-leading startup developing AI for consumer brands. Our solutions leverage machine learning, generative AI, agent-based systems, and graph technologies to get our customers to insights in seconds and to business impact in minutes using our products. We are looking for a Founding Member of Technical Staff to join our team guiding the development and deployment of complex ML systems reporting to Dan Wald , Co-Founder & CAIO. As a Founding Member of Technical Staff, you will work in a hybrid capacity as both a Data Scientist and Machine Learning Engineer, you will play a pivotal role in designing, building, and deploying the intelligence behind our AI products. You’ll work across the full spectrum of applied AI—spanning data science, machine learning, and large-scale production engineering. This hybrid role requires both deep expertise in developing innovative models and the engineering discipline to deploy and maintain them in robust, scalable systems. You’ll collaborate closely with data engineers, product leads, backend engineers, and customer-facing teams to ensure that our AI systems deliver measurable value in real-world environments. As one of the earliest technical hires, you will help define our AI strategy, set technical standards, and establish best practices for applied AI at scale.

Requirements

Proven track record of launching AI/ML products into production.
Experience with core ML/AI tools: Python, PyTorch, TensorFlow / Keras, scikit-learn, SQL, Spark.
Experience writing production-grade Python (object- and function-oriented).
Hands-on expertise with large-scale ML systems, GenAI (LLMs, diffusion), agents, and graph-based models.
Experience designing and managing ML orchestration workflows and versioned pipelines (Airflow, ZenML, Kedro, dbt, etc.).
Strong problem-solving skills, adaptability, and a “hacker” mentality.
Excellent communication skills—able to work with both technical and non-technical stakeholders.
Demonstrated thought leadership and innovation in applied AI.

Responsibilities

Develop and Deploy AI Systems Architect, build, and deploy ML/GenAI products on cloud infrastructure (AWS or similar).
Design and implement end-to-end AI workflows: data ingestion, feature engineering, modeling, evaluation, and deployment.
Create automated pipelines for continuous learning, model promotion, and performance monitoring.
System Architecture & Reliability Lead the design of ML orchestration frameworks (Airflow, Kedro, ZenML, Flyte) to ensure reproducibility and scalability.
Oversee deployment of large-scale and multi-agent AI systems with high reliability and fault tolerance.
Continuously optimize workflows for efficiency, robustness, and performance in production.
Applied Data Science & Business Impact Translate complex business problems into AI solutions, including data collection, experiment design, and roadmap planning.
Develop interpretable, modular, and scalable ML systems that deliver measurable business value.
Work directly with customers and stakeholders to ensure deployed systems achieve their intended impact.
Innovation & Thought Leadership Stay current with advancements in AI/ML, including LLMs, diffusion models, graph AI, and agent architectures.
Propose and prototype new approaches for integrating emerging technologies into production products.
Develop methods to quantify and communicate AI performance and business ROI.
Promote responsible, ethical, and impactful AI practices across the organization.