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 models that power core product features at scale - spanning NLP, ranking, and predictive systems processing millions of daily inferences.
You will work in a small, senior team where infrastructure decisions, model quality, and shipping velocity all matter equally, and your work directly shapes how the platform learns from user behavior.
Key Responsibilities
The role owns the design, training, and production deployment of machine learning models that power core product features at scale - spanning NLP, ranking, and predictive systems processing millions of daily inferences.
You will work in a small, senior team where infrastructure decisions, model quality, and shipping velocity all matter equally, and your work directly shapes how the platform learns from user behavior.
Key Responsibilities
- Design, train, and evaluate ML models for production use cases including ranking, classification, and forecasting, with clear ownership of offline and online metrics
- Build and maintain training and feature pipelines in Python and PySpark, ensuring consistency between training and serving environments
- Deploy models end-to-end using Kubernetes, TorchServe, or managed cloud services (SageMaker, Vertex AI), including canary rollouts and rollback strategies
- Instrument model monitoring for data drift, latency, and prediction quality; build automated alerting and retraining triggers
- Optimize inference performance - quantization, batching, model distillation - to meet strict latency and cost budgets
- Collaborate with data engineering, product, and applied science teams to translate ambiguous problems into well-scoped ML solutions
- Contribute to code reviews, design docs, and shared ML platform tooling used across the organization
- 3–6 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple models shipped to production
- Strong Python engineering skills; comfortable writing production-grade, tested code beyond notebooks
- Deep hands-on experience with PyTorch or TensorFlow, and modern ML tooling (MLflow, W&B, Kubeflow, Airflow)
- Production experience with cloud ML infrastructure on AWS, GCP, or Azure, including containerized deployment (Docker, Kubernetes)
- Solid ML fundamentals: evaluation methodology, feature engineering, regularization, and the failure modes of models in production
- MS or PhD in Computer Science, Statistics, or a related quantitative field, or equivalent industry experience
- Bonus: experience with LLM fine-tuning, distributed training (DeepSpeed, FSDP), real-time streaming systems (Kafka, Flink), or Rust/C inference optimization