ML Engineer

Job Title ML Engineer What You Will Own End-To-End ML Lifecycle Across Real Products. Data Ingestion Feature Design Model Selection Training Deployment Monitoring Iteration. No handoffs. Production-Grade ML Systems built with PyTorch or TensorFlow with attention to latency, reliability, cost and failure modes. Applied GenAI and LLM work where it 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. Translate fuzzy business problems into tractable ML solutions and quantify impact. Technical leadership: code reviews, model reviews, mentoring and raising the bar for ML engineering discipline. Incident ownership: debug production failures, data drift, performance regressions and bias issues calmly and decisively. Required Profile 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. TensorFlow acceptable. Demonstrated experience deploying, maintaining and scaling ML models in production environments. Solid cloud experience across AWS, GCP or Azure. Comfort with Spark, SQL, Docker and Kubernetes. Strong grasp of ML fundamentals: model architectures, optimization tradeoffs, evaluation, design, experimentation rigor. Clear written and verbal communication. Able to explain complex systems without theatrics. Preferred Signals Direct experience with LLM systems in production. Fine‑tuning, RAG, evaluation, safety, 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, anomaly detection. Evidence of technical leadership: open‑source contributions, internal platforms, publications, or scaled internal tools. What Nenu AI Offers 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), unlimited PTO and learning budget. Hybrid Bay Area setup optimized for collaboration without dogma. Work that compounds. Systems that ship. Problems that matter. #J-18808-Ljbffr SupportFinity™