ML Engineer

About the role

Own the end-to-end machine learning lifecycle across real products, from data ingestion to deployment and monitoring.

Responsibilities

• Build production-grade ML systems using PyTorch or TensorFlow, focusing on latency, reliability, cost, and failure modes.
• Apply generative AI and large language models where they create measurable value, including fine-tuning, rag prompt orchestration, evaluation, and guardrails.
• Work with MLOps foundations, including model versioning, CI/CD, automated testing, deployment pipelines, serving layers, monitoring, and experimentation.
• Tight partnership with product engineering and data teams to translate fuzzy business problems into tractable ML solutions and quantify impact.
• Lead technical reviews, model reviews, mentoring, and raising the bar for the ML engineering discipline.
• 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 proficiency in Python, with deep familiarity preferred in PyTorch, and acceptable in TensorFlow.
• Demonstrated experience deploying, maintaining, and scaling ML models in production environments.
• Solid cloud experience across AWS, GCP, or Azure, including comfort with Spark SQL, Docker, and Kubernetes.
• A strong grasp of ML fundamentals, including model architectures, optimization trade-offs, evaluation design, experimentation rigor, and clear communication skills.
• Prioritize direct experience with large language model systems in production, fine-tuning, rag evaluation, safety, cost control, and exposure to MLOps platforms like 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 through code reviews, model reviews, mentoring, and contributions to open-source projects, internal platforms, publications, or scaled internal tools.

Qualifications

Preferred signals include direct experience with large language model systems in production, fine-tuning, rag evaluation, safety, cost control, and exposure to MLOps platforms such as MLflow, Kubeflow, Airflow, or equivalent internal systems.

Skills

Translate fuzzy business problems into tractable ML solutions and quantify impact.

Benefits

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.

Pay

N/A

Schedule

N/A