ML/AI Engineer

You will build machine learning systems that solve real business problems: predictions, recommendations, anomaly detection, and classification. This is not a research role. You take models from prototype to production, own the full lifecycle, and measure success by business outcomes, not just accuracy scores.

• Develop and deploy ML models for classification, regression, recommendation, and anomaly detection
• Build and maintain ML pipelines: data preprocessing, feature engineering, training, evaluation, and serving
• Design experiments, run A/B tests, and measure model impact on business metrics
• Optimize model performance for latency, throughput, and cost in production environments
• Collaborate with data engineers to define feature stores and ensure data quality for model inputs
• Build monitoring for model drift, data quality issues, and prediction accuracy degradation
• Document model architectures, training procedures, and performance benchmarks

• 3+ years of ML engineering experience with models running in production
• Strong Python skills with deep experience in scikit-learn, PyTorch, or TensorFlow
• Understanding of ML fundamentals: bias-variance tradeoff, cross-validation, feature selection, regularization
• Experience with MLOps: model versioning, experiment tracking (MLflow, Weights & Biases), and automated retraining
• Familiarity with cloud ML services: SageMaker, Vertex AI, or Azure ML
• Solid SQL and data manipulation skills (Pandas, Polars, or PySpark)
• Experience deploying models as APIs or batch inference jobs in production
• Clear written and verbal communication in English

• Experience with LLM integration: prompt engineering, fine-tuning, RAG architectures
• Familiarity with deep learning for NLP or computer vision tasks
• Knowledge of real-time inference systems and model optimization (ONNX, TensorRT, quantization)
• Published research or open-source contributions in ML

• Git and GitHub for version control of code, notebooks, and model artifacts
• VS Code, Cursor, or JupyterLab with AI-assisted coding tools (GitHub Copilot or similar)
• MLflow or Weights & Biases for experiment tracking and model registry
• Jira, Linear, or similar for project tracking
• CI/CD pipelines for automated model testing and deployment
• Docker for reproducible training and serving environments
• Cloud ML platforms (SageMaker, Vertex AI) for training and serving infrastructure

• Competitive salary benchmarked to your experience and location
• Fully remote position with flexible working hours
• Equipment budget for your home office setup (GPU access included where needed)
• Annual learning budget for courses, conferences, and certifications
• Health coverage and wellness benefits
• Flexible PTO policy with no approval bottlenecks