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# Principal Machine Learning Engineer

- **Company:** Harnham Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Cloud Computing, Distributed Systems, Machine Learning, Performance Tuning, Azure Machine Learning, Software Engineering, Chatbots, DeepSpeed, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Discretization, Kubernetes, Information Technology, Graphql, Machine Learning Operations, Grpc, Microservices
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_8dbec7e4608228c29dda55616887d768

## About the Role

* PhD in Computer Science, Mathematics, Statistics, or related field
 * Strong experience as an IC in Machine Learning Engineering
 * Proven track record in building and scaling ML/AI systems in production
 * Deep understanding of ML fundamentals (NLP and/or Computer Vision)
 * Experience with distributed systems (Ray, Horovod, DeepSpeed, etc.)
 * Strong software engineering and system design fundamentals
 * Experience owning ML services end-to-end in enterprise environments
 * Ability to align technical work with business outcomes and communicate with stakeholders, * Experience with ML pipelines (Kubeflow, DVC, Ray)
 * Familiarity with microservices (gRPC, GraphQL)
 * Experience with LLM optimization (fine-tuning, quantization, PEFT)
 * Knowledge of advanced prompting strategies (Chain-of-Thought, etc.)

If you're interested in working on AI systems at a massive scale, with real adoption and immediate impact, this is a rare opportunity to do so within a highly respected, well-resourced environment.

## Description

* Design and deploy scalable LLM-powered systems and reusable backend APIs
 * Bridge cutting-edge AI research with production-grade engineering
 * Build agent-based systems, RAG pipelines, and workflow automation tools
 * Develop capabilities across chatbots, text-to-code, and data interaction layers
 * Partner with cloud and SRE teams to deliver robust, scalable architectures
 * Implement distributed systems for training and inference (e.g., Ray, DeepSpeed)
 * Drive best practices for ML systems, deployment, and performance optimization