> Markdown version of [/jobs/ext/4626043-ml-engineer](https://www.wearedevelopers.com/jobs/ext/4626043-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Engineer

- **Company:** Metanet, Inc
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Cloud Computing, Code Review, Continuous Integration, Software Debugging, High-Level Architecture, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Data Ingestion, Pytorch, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Apache Spark, Model Validation, Kubernetes, Information Technology, Modeling and Simulation, Machine Learning Operations, Software Version Control, Docker
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_aa5abc285fe1d93cf0631b12ff1c4e4e

## About the Role

* 7+ years of hands-on ML engineering with clear senior-level ownership of production systems.
 * Strong academic grounding or equivalent applied depth in machine learning, computer science or related fields.
 * Expert Python, deep familiarity with PyTorch (preferred) and TensorFlow (acceptable).
 * Demonstrated experience deploying, maintaining and scaling ML models in production environments.
 * Solid cloud experience across AWS, GCP or Azure. Comfortable with Spark, SQL, Docker and Kubernetes.
 * Strong grasp of ML fundamentals: model architectures, optimisation trade-offs, evaluation, design, experimentation and rigor.
 * Clear written and verbal communication. Able to explain complex systems without theatrics., * Direct experience with LLM systems in production: fine-tuning, RAG, evaluation, safety and cost control.
 * Exposure to MLOps platforms such as MLflow, Kubeflow, Airflow or equivalent internal systems.
 * Depth in one or more domains such as NLP, search, recommendations, forecasting or anomaly detection.
 * Evidence of technical leadership: open-source contributions, internal platform development, publications or scaled internal tools.

## Description

* End-to-End ML Lifecycle across real products: data ingestion, feature design, model selection, training, deployment, monitoring and iteration. No handoffs.
 * Production-grade ML systems built with PyTorch or TensorFlow, focusing on latency, reliability, cost and failure modes.
 * Applied GenAI and LLM work that creates measurable value: fine-tuning, RAG, prompt orchestration, evaluation and guardrails. No hype-first work.
 * MLOps foundations: model versioning, CI/CD, automated testing, deployment pipelines, serving layers, monitoring and A/B experimentation.
 * Tight partnership with product engineering and data teams. Translate fuzzy business problems into tractable ML solutions and quantify impact.
 * Technical leadership: code reviews, model reviews, mentoring and raising the bar for the ML engineering discipline.
 * Incident ownership: debug production failures, data drift, performance regressions and bias issues calmly and decisively.