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 video-ingesting enterprises with domain-specific semantic reasoning.
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 the domain of real-time video understanding. Backed by leading investors and enterprises (such as Pear VC, BAG VC, Confluent and Cognition AI), we’re committed to building cutting-edge infrastructure that helps teams realize the full potential of their video insights.
As a Founding Machine Learning Infrastructure Engineer, you will build and maintain the end-to-end infrastructure that makes our real-time, semantic video reasoning AI agents possible. You’ll architect and optimize our data ingestion pipelines—integrating Kafka and Flink for streaming—as well as robust APIs that facilitate seamless communication between front-end interfaces, ML pipelines, and underlying storage systems. By establishing strong observability practices, CI/CD tooling, and highly scalable backend services, you’ll ensure that our platform can handle dynamic loads and growing complexity without sacrificing latency or reliability.
You’ll also collaborate on research-driven experimentation. Working closely with our team, you’ll support the rapid evaluation of new models and techniques. Your backend and full-stack capabilities will create an environment where novel ML approaches can be seamlessly tested, integrated, and iterated upon. Whether it’s spinning up GPU-accelerated instances for fast inference, fine-tuning backend APIs for new embedding strategies, or streamlining data flows for model comparison experiments, your role will be pivotal in turning research insights into production-ready features.
Design and implement scalable ingestion pipelines using Kafka and Flink to handle real-time text, video, and metadata streams.
Build and maintain backend APIs that interface smoothly with ML components, front-end dashboards, and storage layers.
Integrate observability and CI/CD practices to enable quick iteration, safe rollouts, and immediate feedback loops.
Support the research and experimentation of new ML models, ensuring that backend services and APIs can adapt rapidly to novel requirements.
Collaborate with ML Engineers to ensure that infrastructure, tooling, and workflows accelerate model evolution and performance tuning.
Strong programming skills (Python / Javascript), and experience building backend APIs and services
Prior experience setting up CI/CD pipelines for ML integration
Understanding of ML workflow management and scaling model serving infrastructure
Familiarity with IaC (Docker, Kubernetes, Terraform) and observability tools (Grafana, Prometheus)
Experience integrating with GPU-accelerated platforms for low-latency inference
Familiarity with vector databases, embedding stores, and ML serving frameworks
Proficiency with distributed systems and streaming platforms (Apache, Confluent)
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.
Originally posted on Himalayas
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