Machine Learning Engineer

Evlo AI; Phoenix (AZ); Presencial; USD 120 000 - 165 000; Tempo integral

Resumo da oferta

Evlo AI is seeking an ML Engineer to own end-to-end ML lifecycle from data exploration to production deployment. You will build real-time inference systems with latency and cost constraints, collaborating with data engineers and product teams.

The role requires strong Python, PyTorch or TensorFlow, and SQL skills, with Docker/Kubernetes experience for deploying scalable ML services in cloud environments.

Qualificações

• 3–7 years of experience in ML engineering or related role with multiple models shipped to production.
• Expert-level Python and proficiency with PyTorch, TensorFlow, or scikit-learn.
• Production experience with ML deployment and serving stacks (Docker, Kubernetes, FastAPI, SageMaker, Vertex AI, or equivalent).
• Strong SQL skills and experience building feature/data pipelines with Spark, Airflow, or similar tooling.
• Solid ML fundamentals: evaluation methodology, regularization, handling class imbalance, bias-variance tradeoff.
• Bachelor's degree in Computer Science, Engineering, Statistics, or related quantitative field (or equivalent practical experience).
• Bonus: Experience with LLM/GenAI systems, MLOps tooling, or real-time streaming ML; advanced degrees.

Responsabilidades

• Design, train, and evaluate machine learning models for production use cases with measurable business impact.
• Build and maintain data pipelines and feature stores in Python, SQL, and Spark.
• Deploy and serve models using Docker, Kubernetes, and cloud ML platforms; manage canary rollouts and rollbacks.
• Establish model monitoring and observability with data drift, latency, and quality metrics.
• Optimize inference performance through quantization, batching, caching, and hardware-aware deployment
• Run experiments — A/B tests, offline evaluation, and error analysis — to validate improvements
• Contribute to ML platform tooling, documentation, and engineering standards; mentor junior engineers.

Conhecimentos

Python

PyTorch

TensorFlow

scikit-learn

SQL

Spark

Docker

Kubernetes

FastAPI

MLOps

Formação académica

Bachelor's degree in Computer Science, Engineering, Statistics, or related field

Ferramentas

Docker

Kubernetes

FastAPI

SageMaker

Vertex AI

Kubeflow

MLflow

Airflow

Apache Spark

Descrição da oferta de emprego

About The Role

The role owns the end-to-end machine learning lifecycle — from data exploration and model development to deploying, monitoring, and iterating on models running in production. The team ships ML systems that handle high-volume, real-time inference where latency, accuracy, and cost efficiency are all first-class constraints.

About The Role

The role owns the end-to-end machine learning lifecycle — from data exploration and model development to deploying, monitoring, and iterating on models running in production. The team ships ML systems that handle high-volume, real-time inference where latency, accuracy, and cost efficiency are all first-class constraints.

This is a hands-on engineering role, not a research-only position. The ML engineer will work closely with data engineers, backend teams, and product stakeholders to turn messy, real-world data into reliable ML-powered features that customers depend on daily.

Key Responsibilities

• Design, train, and evaluate machine learning models — including gradient boosting, deep learning, and NLP models — for production use cases with measurable business impact
• Build and maintain data pipelines and feature stores in Python, SQL, and Spark, ensuring consistency between offline training and online serving environments
• Deploy and serve models using Docker, Kubernetes, and cloud ML platforms (AWS, GCP, or Azure), including canary rollouts and rollback strategies
• Establish model monitoring and observability: tracking data drift, latency, and prediction quality with automated alerting and retraining triggers
• Optimize inference performance through model quantization, batching, caching, and hardware-aware deployment (GPU and CPU)
• Run rigorous experiments — A/B tests, offline evaluation, and error analysis — to validate model improvements before release
• Contribute to ML platform tooling, documentation, and engineering standards; mentor junior engineers through code reviews

What We Are Looking For

• 3–7 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple models shipped to production
• Expert-level Python and strong proficiency with PyTorch, TensorFlow, or scikit-learn
• Production experience with ML deployment and serving stacks (Docker, Kubernetes, FastAPI, SageMaker, Vertex AI, or equivalent)
• Strong SQL skills and experience building feature/data pipelines with Spark, Airflow, or similar tooling
• Solid ML fundamentals: evaluation methodology, regularization, handling class imbalance, and the bias-variance tradeoff in practice
• Bachelor's degree in Computer Science, Engineering, Statistics, or a related quantitative field (or equivalent practical experience)
• Bonus: Experience with LLM/GenAI systems (RAG, fine-tuning, vector databases), MLOps tooling (MLflow, W&B, Kubeflow), or real-time streaming ML; Master's or PhD in a relevant field