Founding Machine Learning Engineer - NomadicML
Founding Machine Learning Engineer - NomadicML
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Job Description
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 Snowflake and Lyft they founded NomadicML to solve a critical industry challenge: elevate critical operations of videoingesting enterprises with domainspecific semantic reasoning.
At NomadicML we leverage advanced techniques such as retrievalaugmented generation adaptive finetuning and computeaccelerated inference to significantly improve machine learning models in the domain of realtime video understanding. Backed by leading investors and enterprises (such as Pear VC BAG VC Confluent and Cognition AI) were committed to building cuttingedge 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 next generation of semantic video reasoning AI agents blending cuttingedge research with practical implementation. Youll design implement and refine RetrievalAugmented Generation (RAG) pipelines enabling our models to adapt in realtime to changing data and user needs. This will involve working with text video and other highdimensional inputs as well as exploring advanced embeddings vector databases and GPUaccelerated infrastructures. Youll apply statistical rigorusing significance testing distributional checks and other quantitative methodsto determine precisely when and how to retune models ensuring that updates are timely yet never arbitrary.
Beyond the core ML tasks youll also be a key contributor to our research initiatives. Youll evaluate and experiment with new model architectures foundational models and emerging techniques in largescale machine learning and optimization. As part of the fullstack experience youll work closely with the other team members to build intuitive frontend interfaces dashboards and APIs. These tools will enable rapid iteration realtime monitoring and easy configuration of models and pipelines making it possible for both technical and nontechnical stakeholders to guide model evolution effectively.
Key Responsibilities:
Research prototype and integrate new model architectures and foundational models into our pipeline.
Develop and maintain realtime RAG workflows ensuring efficient adaptation to new text video and streaming data sources.
Implement statistical methods to determine when models need retuning leveraging metrics significance tests and distributional analyses.
Collaborate with Software Engineers to build frontend 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 RetrievalAugmented Generation (RAG) pipelines fine tuning APIs and similar ML workflows.
Strong statistical background for evaluating model performance
Nice 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 largescale adaptation techniques
What We Offer:
Competitive compensation and equity
Apple Equipment
Health dental and vision insurance.
Opportunity to build foundational machine learning infrastructure from scratch and influence the products technical trajectory.
Primarily inperson at our San Francisco office with hybrid flexibility.
Employment Type
Full-Time
