Machine Learning Engineer — Los Angeles, CA — LinkedIn — 4487927978

About The Role

The Machine Learning Engineer will design, build, and deploy production ML systems across the full model lifecycle — from data pipelines and training infrastructure to low-latency inference services. This role focuses on real-world ML at scale: models that must be accurate, fast, and reliable under real production traffic.

The role matters because models sitting in notebooks create no value. This engineer will bridge the gap between experimentation and production, working closely with data scientists, platform engineers, and product teams to ship ML features that users and business stakeholders actually depend on.

Key Responsibilities

• Design, train, and evaluate machine learning models — including gradient boosting, deep learning, and NLP models — for production use cases with measurable business impact

• Build and maintain end-to-end ML pipelines in Python, from data ingestion and feature engineering to model training and validation

• Deploy and serve models in production using Docker, Kubernetes, and cloud ML platforms (AWS SageMaker, GCP Vertex f5, or Azure ML)

• Implement MLOps best practices: experiment tracking (MLflow or Weights & Biases), model versioning, CI/CD for ML, and automated retraining workflows

• Monitor production models for data drift, performance degradation, and latency issues; build alerting and automated rollback mechanisms

• Collaborate with data engineers to design robust feature stores and ensure training/serving consistency

• Conduct rigorous model evaluation using appropriate metrics, A/B testing, and offline/online validation; document findings and communicate results to technical and non-technical stakeholders

What We Are Looking For

• 3–6 years of experience in machine learning engineering, MLOps, or applied data science with production ML deployments

• Expert-level Python and strong proficiency with at least one major ML framework: PyTorch, TensorFlow, or scikit-learn/XGBoost

• Hands-on experience deploying and operating models in production on AWS, GCP, or Azure, including containerization with Docker and orchestration with Kubernetes

• Solid grasp of ML fundamentals: feature engineering, model selection, evaluation metrics, overfitting/regularization, and class imbalance handling

• Experience with ML workflow tooling such as MLflow, kZGqrPk, Kubeflow, or equivalent orchestration platforms

• Bachelor's degree in Computer Science, Engineering, Statistics, or a related quantitative field (or equivalent practical experience)

• Bonus: Experience with LLMs, RAG systems, or vector databases; Spark/PySpark for large-scale data processing; contributions to open-source ML projects; MS or PhD in a relevant field