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We are looking for a Founding Member of Technical Staff to join our team guiding the development and deployment of complex ML systems. 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.
Job Responsibility:
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
Requirements:
Proven track record of launching AI/ML products into production