Description:

Jobgether is seeking a Senior Machine Learning Engineer to lead the deployment of advanced models at scale in the United States.
The role involves collaborating with data scientists, MLOps engineers, and cross-functional teams to transition ML models from concept to production.
Responsibilities include transforming ML prototypes into production-grade solutions, building and managing CI/CD pipelines, and optimizing machine learning systems for performance.
The engineer will monitor live models for performance issues and implement cloud-native solutions using AWS, GCP, Spark, and Kubernetes.
The position requires writing clean, maintainable code and contributing to knowledge sharing within the ML engineering team.

Requirements:

A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field is required.
Candidates must have 5+ years of experience in ML engineering or software engineering with a focus on production ML deployment.
Strong programming skills in Python and experience with frameworks such as TensorFlow, PyTorch, or Scikit-Learn are necessary.
Familiarity with MLOps tools like MLflow, Airflow, Docker, Kubernetes, and CI/CD practices is essential.
Proficiency with cloud platforms, specifically AWS or GCP, including managed ML services is required.
Candidates should have experience with data pipelines and distributed systems like Spark or Dask.
Strong problem-solving skills and the ability to troubleshoot and optimize production ML systems are crucial.
Excellent communication skills and the ability to work effectively in cross-functional teams are mandatory.

The position offers a competitive base salary ranging from $173,000 to $230,000 USD annually, along with performance-based bonuses.
An equity package is provided to align employee contributions with company success.
Employees can work fully remotely with a monthly internet reimbursement.
Comprehensive medical, dental, and vision insurance is included.
Health Savings Account (HSA) and Flexible Spending Account (FSA) options are available.
The company offers Flexible Time Off and generous parental leave policies.
Employees enjoy quarterly Recharge & Reset long weekends and monthly social events.
Additional support includes an Employee Assistance Program (EAP) and wellness resources.
Commuter benefits and volunteer time off are provided to support personal causes.