Machine Learning Engineer

Evlo AI; Seattle (WA); On-site; USD 150,000 - 210,000; Full time

Job summary

Evlo AI is seeking an ML Engineer to own the design, deployment, and scaling of production ML systems powering high-throughput applications. You will collaborate with researchers and data platform engineers to bridge experimentation and reliable production infrastructure.

You will build end-to-end ML infrastructure, implement robust CI/CD pipelines, monitor for drift and performance, and optimize costs across cloud environments with quantization and hardware acceleration.

Qualifications

• 3–6 years of professional software engineering experience, with at least 3 years focused on ML in production.
• Proficiency in Python and deep familiarity with ML frameworks (PyTorch, TensorFlow, or JAX).
• Hands-on experience with cloud infrastructure and MLOps platforms (AWS SageMaker, GCP Vertex AI, or MLflow).
• Strong understanding of distributed systems, data processing pipelines, and containerization (Docker, Kubernetes).
• BS or MS in CS, ML, Statistics, or related field.

Responsibilities

• Design and deploy production-grade ML models with high availability and low latency.
• Build scalable data ingestion and feature engineering pipelines (Python, Spark, distributed compute).
• Implement CI/CD pipelines for ML models with testing, experiment tracking, and model registry tools.
• Monitor deployed models for data and concept drift, with automated alerting and retraining loops.
• Optimize inference costs and resource use across cloud infra using quantization, pruning, and hardware acceleration.
• Collaborate with cross-functional teams to translate business needs into robust ML solutions.

Skills

Python

PyTorch

TensorFlow

JAX

Distributed systems

Docker

Kubernetes

Cloud computing

Education

BS in Computer Science

MS in Machine Learning

Tools

AWS SageMaker

GCP Vertex AI

MLflow

Job description

About The Role

The role owns the design, implementation, and scaling of production machine learning systems powering high-throughput applications.

The engineering team collaborates closely with applied researchers and data platform engineers to bridge the gap between experimental modeling and reliable production infrastructure.

Key Responsibilities

• Design and deploy production-grade machine learning models, ensuring high availability, low latency, and robust fault tolerance
• Build scalable data ingestion and feature engineering pipelines using Python, Apache Spark, and distributed compute frameworks
• Implement robust CI/CD pipelines for ML models, integrating automated testing, experiment tracking, and model registry tools
• Monitor deployed models for data drift, concept drift, and performance anomalies, establishing automated alerting and retraining loops
• Optimize model inference costs and resource utilization across cloud infrastructure using quantization, pruning, and hardware acceleration
• Collaborate with cross-functional teams to translate complex business requirements into robust technical specifications and ML solutions

What We Are Looking For

• 3-6 years of professional software engineering experience, with at least 3 years focused specifically on machine learning engineering in production
• Proficiency in Python and deep familiarity with core ML frameworks such as PyTorch, TensorFlow, or JAX
• Hands-on experience with cloud infrastructure and MLOps platforms including AWS SageMaker, GCP Vertex AI, or MLflow
• Solid understanding of distributed systems, data processing pipelines, and containerization technologies like Docker and Kubernetes
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field
• Bonus: Contributions to open-source ML projects or published research at top-tier AI/ML conferences