Senior ML Engineers

Job Overview

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and deploying machine learning models and systems that power our products and services. You will work on complex data challenges, develop scalable algorithms, and lead ML projects from conception to deployment. In this senior role, you will collaborate with cross-functional teams, mentor junior engineers, and drive innovation in machine learning and AI.

Key Responsibilities

Model Development: Design, develop, and optimize machine learning models for various use cases, ensuring they are scalable and robust.

Algorithm Selection: Select and implement appropriate ML algorithms (e.g., supervised, unsupervised, reinforcement learning) based on the problem at hand.

Data Pipeline Management: Build and manage data pipelines to efficiently gather, preprocess, and feed large datasets into ML models.

Deployment and Scaling: Lead the deployment of ML models in production environments, ensuring scalability and performance optimization.

Collaboration: Work closely with data scientists, software engineers, and product managers to align ML initiatives with business goals.

Mentorship: Provide guidance and mentorship to junior ML engineers, fostering growth and development within the team.

Experimentation and Validation: Conduct experiments to validate the performance of models, fine-tune hyperparameters, and ensure models are generalizable to real-world data.

Monitoring and Maintenance: Continuously monitor the performance of deployed models and fine-tune or retrain them as necessary.

Innovation and Research: Stay up to date with the latest research and advancements in machine learning, and apply cutting-edge techniques to improve our systems.

Requirements

Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field (PhD is a plus).

Experience:

5+ years of experience working in machine learning or AI roles, with a strong track record of deploying ML models in production environments.

Experience with a range of ML algorithms (e.g., regression, decision trees, neural networks, clustering, NLP).

Technical Skills:

Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).

Strong understanding of data structures, algorithms, and software engineering principles.

Experience with cloud-based ML platforms (AWS, GCP, Azure) and distributed computing (e.g., Spark, Hadoop).

Familiarity with MLOps practices (CI/CD, automated testing, version control for models).

Strong skills in working with large datasets and data pipelines (e.g., SQL, NoSQL, Kafka, Airflow).

Problem-Solving Skills: Exceptional analytical and problem-solving skills, with experience in breaking down complex ML problems and implementing effective solutions.

Communication Skills: Ability to communicate complex technical ideas clearly and effectively to both technical and non-technical stakeholders.

Preferred Qualifications

Experience in developing deep learning models, especially in NLP, computer vision, or reinforcement learning.

Familiarity with AutoML frameworks and tools.

Contributions to open-source ML projects or publications in leading conferences.

Strong background in mathematics and statistics, including linear algebra, probability, and optimization techniques.

Experience leading ML teams or projects from concept to deployment.