# Senior ML Engineer

**Company:** [Symphony Solutions](http://jobs.workable.com/companies/9o3HSXG1digSEp5ARNTfZQ.md)
**Location:** Remote
**Workplace:** remote
**Employment type:** Full-time
**Department:** MSEC

[Apply for this job](http://jobs.workable.com/view/97cf0aec-cfe2-405e-b623-5f3181052b96)

## Description

We are looking for a Senior ML Engineer to design, build, and optimize machine learning models and pipelines powering production systems. The ideal candidate brings deep hands-on experience across the ML lifecycle, with particular strength in recommender systems, deep learning, MLOps practices, and cloud-based ML infrastructure on AWS.

## Requirements

-   4+ years of hands-on experience in machine learning engineering
-   Strong proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost, etc.).
-   Solid experience with deep learning — architecture design, training, hyperparameter tuning, and deployment of neural network models.
-   Proven experience designing and deploying recommender systems.
-   Hands-on experience with AWS SageMaker and broader AWS ML ecosystem.
-   Practical experience setting up data processing and ML workflows on AWS.
-   Strong MLOps skills.
-   Solid understanding of the full ML lifecycle.
-   Hands-on experience with containerization and orchestration in production environments.
-   Proficiency with SQL and experience working with both structured and unstructured data sources.
-   Strong problem-solving skills with an emphasis on scalability and performance optimization.

### Responsibilities:

-   Design, train, and iterate on ML and deep learning models for recommendation, ranking, and personalization use cases.
-   Architect and maintain end-to-end ML pipelines on AWS.
-   Set up and optimize data processing and ML workflows using AWS services.
-   Build and maintain MLOps infrastructure.
-   Collaborate with data engineers to ensure data quality, build feature stores, and prepare datasets for model training and inference.
-   Evaluate and benchmark model performance, run offline and online experiments, and drive continuous improvement of model accuracy and efficiency.
-   Optimize model serving infrastructure for latency, throughput, and cost-effectiveness.
-   Partner with product and business stakeholders to translate requirements into well-scoped ML solutions.
-   Document model architecture, assumptions, performance characteristics, and known limitations.
-   Stay current with advances in recommendation systems, deep learning, and cloud ML services, and propose improvements to existing approaches.