Job details

Founding Engineer - ML

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: bridging the performance gap between model development and production deployment.

At NomadicML, we leverage advanced techniques—such as retrieval-augmented generation, adaptive fine-tuning, and compute-accelerated inference—to significantly improve machine learning models in domains like video generation, healthcare, and autonomous systems. Backed by Pear VC and BAG VC, early investors in Doordash, Affinity, and other top Silicon Valley companies, we’re committed to building cutting-edge infrastructure that helps teams realize the full potential of their ML deployments.

About the Role:

As a Founding Machine Learning Engineer, you will shape the next generation of continuously improving AI systems, blending cutting-edge research with practical implementation. You’ll design, implement, and refine Retrieval-Augmented Generation (RAG) pipelines, enabling our models to adapt in real-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 significance testing, distributional checks, and other quantitative methods—to determine precisely when and how to retune models, ensuring that updates are timely yet never arbitrary.

Beyond 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 intuitive front-end interfaces, dashboards, and APIs. These tools will enable rapid iteration, real-time monitoring, and easy configuration of models and pipelines, making it possible for both technical and non-technical stakeholders to guide model evolution effectively.

Key Responsibilities:

Research, prototype, and integrate new model architectures and foundational models into our pipeline.
Develop and maintain real-time 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 front-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 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 large-scale 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 product’s technical trajectory.
Primarily in-person at our San Francisco office with hybrid flexibility.

Average salary estimate

$125000 / YEARLY (est.)

min

max

$100000K

$150000K

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What You Should Know About Founding Engineer - ML, Pear VC

At NomadicML, we're on an exciting journey, and we're looking for a Founding Engineer specializing in Machine Learning to join our dynamic team in Austin, TX. If you’re passionate about pushing the boundaries of AI, you'll love the impact you can have here. You’ll dive right into designing innovative Retrieval-Augmented Generation (RAG) pipelines, a crucial piece of our mission to bridge the gap between model development and production deployment. Your expertise in Python, along with a deep understanding of machine learning model development, will allow you to work with high-dimensional inputs like text and video while exploring advanced techniques in statistical rigor. Not only will you implement real-time RAG workflows, but you’ll also ensure our models adapt seamlessly to changing user needs and data. But it doesn’t stop there! You'll collaborate with our talented engineers to create intuitive front-end interfaces and dashboards that ensure all stakeholders—both technical and non-technical—can effectively engage with our models. Here at NomadicML, you have the unique opportunity to shape foundational machine learning infrastructure from the ground up, significantly influencing our technical trajectory. If you’re ready to take your machine learning career to the next level and work on cutting-edge projects with a passionate team, we can’t wait to meet you!

Frequently Asked Questions (FAQs) for Founding Engineer - ML Role at Pear VC

What are the main responsibilities of a Founding Engineer - ML at NomadicML?

As a Founding Engineer specializing in Machine Learning at NomadicML, your key responsibilities will include designing and implementing Retrieval-Augmented Generation (RAG) pipelines, researching new model architectures, and evaluating model performance using statistical methods. You will also collaborate with software engineers to build front-end interfaces for monitoring and updating models, ensuring our systems are continuously adapting to real-time data.