Founding Machine Learning Engineer NomadicML

Work set-up: 
Full Remote
Contract: 
Full time
Experience: 
Entry-level / graduate
Work from: 
United States

Offer summary

Qualifications:

Proficiency in Python and deep understanding of machine learning model development., Experience with Retrieval-Augmented Generation (RAG) pipelines and fine-tuning APIs., Strong statistical skills for evaluating and retuning models., Knowledge of large-scale ML frameworks like PyTorch or TensorFlow is a plus..

Key responsibilities:

  • Research and integrate new model architectures into the pipeline.
  • Develop and maintain real-time RAG workflows for diverse data sources.
  • Implement statistical methods to determine model retuning needs.
  • Collaborate on building interfaces and dashboards for monitoring and model updates.

Pear VC logo
Pear VC Private Equity & Venture Capital Startup https://www.pear.vc/
11 - 50 Employees
See all jobs

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), we’re 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. You’ll 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. 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 largescale machine learning and optimization. As part of the fullstack experience, you’ll 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:

Required profile

Experience

Level of experience: Entry-level / graduate
Industry :
Private Equity & Venture Capital
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Hard Skills

Python (Programming Language)Statistical AnalysisComputer SystemsApplication Programming Interface (API)EmbeddingMLflowVector DataCI/CDTensorFlowKubeflowPyTorch (Machine Learning Library)
Are you interested?

Machine Learning Engineer Related jobs

See more Machine Learning Engineer jobs