Machine Learning Engineer

About The Role

The role owns the design, training, and production deployment of machine learning systems that power core product functionality - recommendation, ranking, and predictive models serving high-traffic, low-latency environments.

You will join a well-funded applied ML team that treats models as software: versioned, tested, monitored, and continuously improved. The problems are real, the data is messy, and model quality directly drives revenue.

Key Responsibilities

• Design, train, and productionize ML models - ranking, classification, and forecasting - using Python, PyTorch, and scikit-learn
• Build and maintain feature pipelines in PySpark and SQL, ensuring parity between training and online serving environments
• Deploy models to production on AWS (SageMaker, ECS, Lambda) with proper versioning, canary rollouts, and rollback strategies
• Instrument model monitoring for data drift, latency, and performance regression using Prometheus, Grafana, and automated alerting
• Optimize inference performance through quantization, batching, and model distillation where latency budgets demand it
• Partner with data engineers and product managers to translate business problems into well-scoped ML problems with measurable success criteria
• Contribute to shared ML infrastructure: experiment tracking (MLflow), CI/CD for models, and reusable training pipelines

What We Are Looking For

• 3–7 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple models shipped to production
• Strong Python engineering skills; comfort writing production-quality, tested code (not just notebooks)
• Deep hands-on experience with PyTorch or TensorFlow and modern ML tooling (MLflow, Airflow, Kubeflow, or equivalent)
• Production experience with AWS or GCP ML infrastructure, including containerized serving (Docker, Kubernetes)
• Solid ML fundamentals: evaluation design, feature engineering, regularization, and handling class imbalance / skewed data
• MS or PhD in Computer Science, Statistics, Applied Math, or equivalent practical experience
• Bonus: experience with LLM fine-tuning and serving (vLLM, LoRA), real-time streaming systems (Kafka, Flink), or recommendation systems at scale