Job description

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