Machine Learning Engineer

Evlo AI; Raleigh (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI in Raleigh, NC seeks an ML Engineer to own design, training, and deployment of systems powering core features across tabular ML, NLP, and LLM workflows.

You will tackle latency, reliability, and scale in production, shipping systems beyond notebooks and collaborating with senior ML engineers and data scientists.

Qualifications

• 3–7 years of experience in machine learning engineering, with multiple production deployments.
• Expert-level Python and hands-on in PyTorch, TensorFlow, or scikit-learn.
• Production experience with cloud ML platforms and MLOps tooling (MLflow, model registries, CI/CD for ML).
• Strong ML fundamentals: feature engineering, evaluation design, drift handling, and regularization.
• Containerized deployment experience (Docker, Kubernetes) and serving systems (TorchServe, Triton, FastAPI).
• BS or MS in Computer Science, Statistics, Engineering, or equivalent practical experience.

Responsibilities

• Design, train, and evaluate ML models for production workloads (classification, ranking, NLP, recommendations).
• Build and maintain feature pipelines in Python and SQL for training and online serving paths.
• Deploy and version models using Docker, Kubernetes, and cloud platforms with CI/CD automation.
• Instrument models for monitoring: drift, latency, and business metrics with alerts and retraining triggers.
• Collaborate with data engineering to build reliable data pipelines using Spark, Airflow, and modern data tools.
• Introduce and champion LLM-based capabilities (RAG, embeddings, fine-tuning) with rigorous evaluation.
• Contribute to code reviews, design docs, and ML engineering standards.

Skills

Python

PyTorch

Cloud ML

MLOps

Docker

Kubernetes

SQL

CI/CD for ML

Education

BS/MS in CS/Statistics/Engineering

Tools

MLflow

Model registries

CI/CD for ML

Job description

About The Role

The role owns the design, training, and production deployment of machine learning systems that directly power core product features - spanning tabular ML, NLP, and increasingly LLM-based workflows.

Working in a small, senior team of ML engineers and data scientists, this person will tackle problems where model performance, inference latency, and reliability all matter - and ship systems that run in production, not just in notebooks.

Key Responsibilities

• Design, train, and evaluate ML models (classification, ranking, NLP, and recommendation use cases) for production workloads
• Build and maintain feature pipelines in Python and SQL, ensuring consistency between training and online serving paths
• Deploy and version models using Docker, Kubernetes, and cloud ML platforms (SageMaker, Vertex AI, or Azure ML), with automated CI/CD for model releases
• Instrument models for monitoring: track drift, latency, and business metrics with alerting and automated retraining triggers
• Collaborate with data engineering to build reliable data pipelines using Spark, Airflow, and modern data stack tooling
• Introduce and champion LLM-based capabilities where they add value - RAG systems, embeddings, fine-tuning - with rigorous evaluation
• Contribute to code reviews, design docs, and the team's ML engineering standards

What We Are Looking For

• 3–7 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple production model deployments
• Expert-level Python and hands-on depth in at least one major framework: PyTorch, TensorFlow, or scikit-learn
• Production experience with cloud ML platforms and MLOps tooling (MLflow, model registries, CI/CD for ML)
• Strong ML fundamentals: feature engineering, evaluation design, regularization, and handling class imbalance and drift
• Experience with containerized deployment (Docker, Kubernetes) and serving systems such as TorchServe, Triton, or FastAPI
• MS or BS in Computer Science, Statistics, Engineering, or equivalent practical experience