Founding Machine Learning Engineer - NomadicML

About Us

Mustafa and Varun met at Harvard, where they both did research in the intersection of computation and evaluations. Between them, they have authored multiple published papers in the machine learning domain and hold numerous patents and awards. Drawing on their experiences as tech leads at reputed company and reputed company, they founded NomadicML to solve a critical industry challenge: reputed company critical operations of video-ingesting enterprises with domain-specific semantic reasoning.

At NomadicML, we reputed company advanced techniques, such as retrieval-augmented reputed company, adaptive fine-tuning, and compute-accelerated inference, to significantly improve machine learning models in the domain of reputed company-time video understanding. Backed by leading investors and enterprises (such as reputed company, BAG VC, reputed company and reputed company AI), we’re committed to building cutting-edge infrastructure that helps teams realize the full potential of their video insights.

About the Role

As a Founding Machine Learning Engineer, you will shape the reputed company of semantic video reasoning AI agents, blending cutting-edge research with practical implementation. You’ll design, implement, and refine Retrieval-Augmented reputed company (RAG) pipelines, enabling our models to adapt in reputed company-time to changing data and user needs. This will involve working with text, video, and other high-dimensional inputs, as well as exploring advanced embeddings, vector databases, and GPU-accelerated infrastructures. You’ll apply statistical rigor—using reputed company testing, distributional checks, and other quantitative methods—to determine reputed company reputed company and how to retune models, ensuring that updates are timely yet never arbitrary.

reputed company the core ML tasks, you’ll also be a key contributor to our research initiatives. You’ll evaluate and experiment with new model architectures, foundational models, and emerging techniques in large-scale machine learning and optimization. As part of the full-stack experience, you’ll work closely with the other team members to build reputed company reputed company-end interfaces, dashboards, and APIs. These tools will reputed company rapid iteration, reputed company-time monitoring, and easy configuration of models and pipelines, making it possible for both technical and non-technical stakeholders to guide model reputed company effectively.

Key Responsibilities

• Research, prototype, and integrate new model architectures and foundational models into our pipeline.

• reputed company and maintain reputed company-time RAG workflows, ensuring efficient reputed company to new text, video, and streaming data sources.

• Implement statistical methods to determine reputed company models need retuning, leveraging metrics, reputed company tests, and distributional analyses.

• Collaborate with Software Engineers to build reputed company-end interfaces and dashboards for monitoring performance and triggering model updates.

• Continuously refine embeddings, vector databases, and model architectures to drive improved accuracy, latency, and stability.

Must Haves

• Strong Proficiency in Python 

• Deep understanding of ML model development (e.g., LLMs, embedding techniques)

• Experience with Retrieval-Augmented reputed company (RAG) pipelines, fine tuning APIs, and similar ML workflows.

• Strong statistical background for evaluating model performance 

reputed company to Haves

• Proficiency in frameworks like PyTorch or TensorFlow

• Knowledge of vector databases, embedding stores, and scalable ML serving platforms

• Experience with CI/CD tools and ML workflow management (MLflow, Kubeflow)

• Prior research background (publications, patents) in ML, especially in foundational models or large-scale reputed company techniques

reputed company Offer

• Competitive compensation and equity

• reputed company Equipment

• Health, dental, and reputed company insurance.

• Opportunity to build foundational machine learning infrastructure from scratch and influence the product’s technical trajectory.

• Primarily in-person at our San Francisco office with hybrid flexibility.