Senior Machine Learning Engineer

Boardroom Appointments; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

A leading company is seeking a Senior Machine Learning Engineer to design, develop, and optimize machine learning models. You will build scalable ML pipelines, collaborate with diverse teams, and implement MLOps best practices to drive innovation. This position requires strong Python skills and at least 5 years of experience in machine learning and deep learning deployments. Join us to leverage cutting-edge technologies and contribute to impactful AI solutions.

Qualifications

• 5+ years experience in machine learning and AI model deployment.
• Strong programming skills in Python with relevant ML libraries.
• Hands-on experience with cloud services and MLOps tools.

Responsibilities

• Design and optimize machine learning models for real-world applications.
• Build scalable ML pipelines and deploy models to production.
• Collaborate with cross-functional teams to align solutions with business objectives.

Skills

Machine Learning

Deep Learning

Python

Cloud Computing

Big Data

Problem-Solving

Communication

Education

Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field

Tools

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

AWS

GCP

Job description

About the job Senior Machine Learning Engineer

Key Responsibilities:

• Model Development & Optimization: Design, develop, and optimize machine learning models for real-world applications, ensuring high accuracy, scalability, and efficiency.
• ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).

Feature Engineering & Data Processing: Collaborate with data engineers to preprocess, clean, and transform large datasets for training and inference.

Productionization: Deploy ML models into production, monitor performance, and continuously improve them through A/B testing and retraining.

Collaboration: Work closely with cross-functional teams including software engineers, product managers, and business stakeholders to align ML solutions with business objectives.

MLOps & Automation: Implement MLOps best practices, automate model training and deployment, and ensure reproducibility.

Performance Monitoring: Develop and maintain monitoring tools to track model performance, drift, and reliability in production.

Research & Innovation: Stay updated with the latest trends and advancements in AI/ML, and integrate cutting-edge research into business solutions.

Required Qualifications & Skills:

Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus.

Experience: Minimum 5+ years of experience in machine learning, deep learning, and AI model deployment in production environments.

Programming: Strong proficiency in Python, with experience in libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.

Cloud & Infrastructure: Hands-on experience with cloud services (AWS, GCP, Azure) and MLOps tools like Kubeflow, MLflow, or SageMaker.

Big Data & Databases: Experience with Spark, Hadoop, SQL, and NoSQL databases for handling large-scale datasets.

DevOps & CI/CD: Familiarity with Git, Docker, Kubernetes, and CI/CD pipelines for ML model deployment.

Algorithm Development: Strong knowledge of ML algorithms, deep learning architectures (CNNs, RNNs, Transformers), and optimization techniques.

Problem-Solving: Strong analytical and problem-solving skills with the ability to design innovative ML solutions for complex business challenges.

Excellent Communication: Ability to explain technical concepts to non-technical stakeholders and document ML processes effectively.

Preferred Qualifications:

Experience with NLP, Computer Vision, or Reinforcement Learning.

Hands-on experience with AutoML, hyperparameter tuning, and model interpretability.

Experience with real-time ML applications and edge AI.

Contributions to open-source ML frameworks or research publications.