ML Engineer

Metanet, Inc

Palo Alto, CA, United States

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

7 years minimum

Working hours

Regular working hours

Job source

www.juju.com

Tech stack

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

+14 more

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

Job 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.

Requirements

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.

Benefits & conditions

Meaningful ownership over core AI systems, not edge experiments.
Compensation aligned to senior impact, not titles.
Performance bonus in the 10-20% range plus modest equity aligned to company stage.
Full benefits including health, dental, vision, 401(k) and unlimited PTO.
Learning budget and a hybrid Bay Area setup optimized for collaboration without dogma.
Work that compounds: systems that ship, problems that matter.

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