Senior Machine Learning Engineer

What You Will Own

• End-to-end ML lifecycle across real products.
• Data ingestion, feature design, model selection, training, deployment, monitoring, iteration.
• 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.
• 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, 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, 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, learning budget.
• Hybrid Bay Area setup optimized for collaboration without dogma.
• Work that compounds. Systems that ship. Problems that matter.