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# Machine Learning Engineer

- **Company:** EVLO, INC.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Code Review, Distributed Computing Environment, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Pytorch, Large Language Models, Deep Learning, Backend, Containerization, Kubernetes, Information Technology, Low Latency, Hardware Acceleration, Machine Learning Operations, Data Pipelines, Docker
- **Published:** August 19, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pdl2ylvn1w

## About the Role

* 3-6 years of professional software and machine learning engineering experience with a track record of deploying models to production
 * Strong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlow
 * Solid understanding of MLOps best practices, containerization with Docker, and orchestration using Kubernetes
 * Experience with cloud infrastructure (AWS, GCP, or Azure) and modern feature stores or vector databases
 * Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related quantitative field
 * Bonus: Experience fine-tuning large language models, contributing to open-source ML projects, or publishing research at top-tier AI conferences

## Description

The role owns the architecture, development, and scaling of machine learning systems, driving the transition of advanced AI models from research into high-throughput production environments.

The team collaborates closely with applied scientists and backend engineers to ensure models achieve optimal performance, low latency, and robust reliability under heavy enterprise workloads., * Design and implement scalable machine learning pipelines for model training, validation, and inference using Python, PyTorch, and distributed computing frameworks

 * Deploy, monitor, and scale models on cloud platforms like AWS SageMaker or GCP Vertex AI with automated CI/CD pipelines
 * Optimize model inference latency, throughput, and memory footprint through quantization, pruning, and hardware acceleration techniques
 * Build feature and data ingestion pipelines handling large-scale datasets, ensuring consistency between training and production feature stores
 * Implement comprehensive monitoring frameworks to track model performance, data drift, and anomaly detection in real-time production environments
 * Write rigorous unit and integration tests, conduct code reviews, and establish engineering best practices for the broader machine learning team


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