ML Engineer

Job Title: ML Engineer

Job Details

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 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 Learning Budget.
• Hybrid Bay Area Setup Optimized For Collaboration Without Dogma.
• Work That Compounds. Systems That Ship. Problems That Matter.