Machine Learning Engineer

About The Role

The role owns end-to-end development of production ML systems: training, deployment, and monitoring of models that power core product features at scale. Work spans classical ML, deep learning, and LLM-powered features, with direct ownership of what ships to production.

The team operates at the intersection of research and engineering — models here must be accurate, fast, cost-efficient, and reliable. This role sits in a well-funded, growth-stage environment where ML infrastructure decisions directly shape product outcomes.

Key Responsibilities

• Design, train, and evaluate ML models for production use cases spanning ranking, classification, NLP, and generative AI features
• Build and maintain training and feature pipelines in Python, PySpark, and Airflow with clear data versioning and reproducibility standards
• Deploy and serve models using Docker, Kubernetes, and cloud ML platforms (SageMaker, Vertex AI, or equivalent), owning latency and cost targets
• Implement LLM-based features including RAG pipelines, prompt orchestration, and evaluation harnesses where applicable
• Establish monitoring for data drift, model degradation, and serving anomalies with automated alerting and retraining triggers
• Collaborate with data engineers, product managers, and applied scientists to translate business problems into well-scoped ML solutions
• Contribute to ML platform improvements: experiment tracking, CI/CD for models, and shared tooling

What We Are Looking For

• 3–6 years of experience in machine learning engineering or applied ML, with multiple models shipped to production
• Strong Python engineering skills plus hands-on depth in PyTorch or TensorFlow
• Experience deploying and operating models on AWS, GCP, or Azure with real production SLAs
• Solid ML fundamentals: evaluation methodology, regularization, handling class imbalance, and offline/online metric alignment
• Experience with MLflow, Weights & Biases, or equivalent experiment tracking, and CI/CD for ML workflows
• Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or equivalent practical experience
• Bonus: experience fine-tuning or serving open-source LLMs, vector databases, Kubernetes-based model serving, or a published paper or open-source ML contribution