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Machine Learning Software Engineer, Generative AI - Google
ML Engineer
Experienced ML software engineer to design and scale trust‑and‑safety systems for generative AI, focusing on classification, fraud detection, and adversarial robustness using Python, LLM frameworks, and cloud infrastructure.
About the role
Key Responsibilities
- Design, implement, and maintain large‑scale machine‑learning pipelines for classification, fraud detection, identity verification, and adversarial robustness in generative AI products.
- Develop safety architectures and monitoring tools for large language models, ensuring compliance with trust‑and‑safety standards.
- Collaborate with cross‑functional engineering groups to align technical direction, influence design decisions, and drive consensus across matrixed teams.
- Optimize data ingestion, preprocessing, and model serving on high‑performance compute infrastructure, leveraging Kubernetes and Google Cloud Platform.
- Mentor junior engineers and contribute to best‑practice documentation for ML infrastructure and safety processes.
Requirements
- 8+ years of software development or machine‑learning infrastructure experience, with a strong focus on trust‑and‑safety or risk‑detection domains.
- Proficiency in Python and deep‑learning frameworks such as TensorFlow or PyTorch.
- Hands‑on experience building and scaling industrial‑grade data pipelines, orchestration systems, and containerized workloads (Kubernetes, GCP).
- Demonstrated ability to lead technical alignment across large, matrixed engineering organizations.
- Bachelor’s degree or equivalent practical experience.
Skills
pythontensorflowpytorchkubernetes