Senior Machine Learning Engineer

Job Description

Job brief

Senior Machine Learning Engineer

Minimum Years of Experience Required: 6+ Years

Salary range: $160,000 - $190,000 (for Senior/Experienced level)

Authorization to Work: We require candidates to be a Permanent Resident or currently authorized to work in the United States when applying. We cannot provide visa sponsorship.

About the Role: We are looking for an experienced Senior Machine Learning Engineer to design, build, and deploy robust and scalable machine learning models and systems in a production environment. You will work on challenging problems at the intersection of software engineering and machine learning, contributing to the development and operationalization of intelligent applications. This role requires strong programming skills, a deep understanding of ML principles, and experience with MLOps practices.

Key Responsibilities:

Design, develop, train, and evaluate machine learning models for various applications, working closely with data scientists and researchers.

Build and maintain scalable MLOps pipelines for automated model training, versioning, deployment, and monitoring in production.

Collaborate with data scientists and software engineers to integrate ML models and inference services into core products and services.

Optimize ML models for performance, efficiency, and scalability in production.

Implement monitoring and alerting for ML models and infrastructure to detect and address issues in real-time.

Stay up-to-date with the latest advancements in machine learning and AI, and evaluate new technologies and techniques.

Contribute to the development and improvement of our ML infrastructure and tools.

Troubleshoot and debug issues in ML pipelines and production models.

Qualifications:

Minimum 6 years of experience in machine learning engineering or a similar role with a strong focus on ML system development.

Strong programming skills in Python and extensive experience with major ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

Experience with MLOps tools and practices (e.g., Docker, Kubernetes, Kubeflow, MLflow, CI/CD for ML).

Solid understanding of machine learning algorithms, model training techniques, and evaluation metrics.

Experience with deploying and serving ML models in a production environment at scale.

Familiarity with cloud platforms (AWS, Azure, GCP) and distributed computing for ML.

Strong software engineering fundamentals, including data structures, algorithms, and system design.

Excellent problem-solving skills and the ability to work effectively in a collaborative team environment.

Benefits:

Paid Parental Leave

Stock Options or RSU program

Comprehensive Health, Dental, and Vision Insurance

Flexible Work Arrangements

Generous Paid Time Off and Holidays

401(k) with company match

Professional Development Stipend

Full-time