What are the responsibilities and job description for the Machine Learning Engineer position at Evlo AI?
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
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
- 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