ML Engineer

MetAntz; Palo Alto (CA); Hybrid; USD 180,000 - 280,000; Full time

Benefits offered by this job

Performance bonus

Health insurance

Dental insurance

Vision insurance

401(k) plan

Unlimited PTO

Learning budget

Job summary

A leading AI solutions provider in Palo Alto is looking for a Senior ML Engineer to take ownership of the entire machine learning lifecycle. The ideal candidate will have over 7 years of experience in ML engineering, a strong background in Python, and significant expertise with production systems using PyTorch. You will work closely with product and data teams to create impactful machine learning solutions, debug complex production issues, and mentor junior engineers. Competitive compensation and excellent benefits available.

Qualifications

• 7+ years of hands-on ML engineering with senior-level ownership of production systems.
• Strong academic grounding or equivalent applied depth in machine learning.
• Expert in Python with deep familiarity of PyTorch and TensorFlow.
• Experience deploying and scaling ML models in production environments.
• Solid cloud experience across AWS, GCP or Azure.
• Clear written and verbal communication.
• Clear written and verbal communication.

Responsibilities

• Lead end-to-end ML lifecycle across real products.
• Build production-grade ML systems focusing on latency and reliability.
• Partner with product engineering and data teams.
• Provide technical leadership through mentoring and code reviews.
• Debug production failures and performance regressions.
• Technical leadership: code reviews, model reviews, mentoring and raising the bar for ML engineering.
• Incident ownership: debug production failures, data drift, performance regressions and bias issues.

Skills

ML engineering

Python

PyTorch

TensorFlow

MLOps

AWS

GCP

Azure

Docker

Kubernetes

Education

Relevant background in machine learning or computer science

Tools

Spark

Job description

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 and iteration. No handoffs.
• Production‑grade ML systems built with PyTorch or TensorFlow, focusing on latency, reliability, cost and failure modes.
• Applied GenAI and LLM work that 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 teams. Translate fuzzy business problems into tractable ML solutions and quantify impact.
• Technical leadership: code reviews, model reviews, mentoring and raising the bar for the 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) and TensorFlow (acceptable).
• Demonstrated experience deploying, maintaining and scaling ML models in production environments.
• Solid cloud experience across AWS, GCP or Azure. Comfortable with Spark, SQL, Docker and Kubernetes.
• Strong grasp of ML fundamentals: model architectures, optimisation trade‑offs, evaluation, design, experimentation and 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 and 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 or anomaly detection.
• Evidence of technical leadership: open‑source contributions, internal platform development, 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) and unlimited PTO.
• Learning budget and a hybrid Bay Area setup optimized for collaboration without dogma.
• Work that compounds: systems that ship, problems that matter.