Вакансия ML Engineer

Skills

• Python
• Git
• TensorFlow
• PyTorch
• scikit-learn
• Linux
• Unix
• Docker
• Kubernetes
• AWS
• Azure
• GCP
• CI/CD
• Jenkins
• GitLab CI
• GitHub Actions
• MLflow
• Kubeflow
• API
• C++

Описание вакансии

Svitla Systems Inc. is looking for a ML Engineer for a full-time position (40 hours per week) in Ukraine. Our client is an early-stage AI startup headquartered in Columbia, South Carolina. It is a human-centered artificial intelligence company dedicated to developing practical and approachable AI tools for real-world applications. Headquartered in Columbia, South Carolina, with a distributed team across the U.S., it is led by a founding team of tech entrepreneurs with over a decade of experience. The company is dedicated to creating AI that enhances, rather than replaces, human intelligence. The company is currently developing an AI platform to empower knowledge workers, creatives, and small businesses.. They develop a human-centered, emotionally aware AI chat agent with customizable personalities that can serve as a witty companion, insightful consultant, or strategic advisor. Their technology supports multiple GPU platforms (ROCm, Metal, CUDA) and emphasizes creating natural, human-like interactions.

Requirements:

• 2 years of professional experience with machine learning (ML).
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science (with a strong engineering/CS focus), or a closely related technical field.
• Understanding of software engineering principles, including data structures, algorithms, object-oriented design, and system design.
• Knowledge of Python for software development, scripting, and machine learning applications.
• Understanding core machine learning concepts, standard algorithms, and the model development lifecycle.
• Experience with version control systems, notably Git.
• Strong analytical and problem-solving skills, with an ability to debug complex systems.
• Excellent communication and collaboration skills, with the ability to work effectively in a team environment.
• A proactive and enthusiastic approach to learning new technologies and engineering practices in the ML domain.

Nice to have:

• Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Familiarity with Linux/Unix environments, shell scripting, and command-line tools.
• Exposure to containerization technologies (e.g., Docker) and orchestration (e.g., Kubernetes).
• Basic understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and their ML/AI services.
• Knowledge of CI/CD pipelines and tools (e.g., Jenkins, GitLab CI, GitHub Actions).
• Familiarity with MLOps concepts, tools (e.g., MLflow, Kubeflow), and best practices.
• Experience with API development and integration (e.g., REST, gRPC).
• Knowledge of C++ for performance-critical components in ML systems.
• Basic understanding of CUDA programming and GPU architecture for accelerating ML workloads.
• Previous internship experience in machine learning engineering, software engineering, or a related role.

Responsibilities:

• Assist in developing, implementing, and maintaining machine learning pipelines for model training, evaluation, deployment, and monitoring.
• Integrate machine learning models into production applications and services to ensure reliability and optimal performance.
• Contribute to the development of MLOps tools and infrastructure for model versioning, management, and automation.
• Help optimize machine learning models and inference pipelines for speed, scalability, and resource efficiency.
• Implement testing strategies and quality assurance measures for machine learning (ML) systems.
• Troubleshoot and resolve issues related to the deployment and operation of machine learning models.
• Apply software engineering best practices (e.g., version control with Git, code reviews, unit/integration testing, CI/CD) to ML projects.
• Create and maintain clear documentation for ML system designs, deployment procedures, and operational playbooks.
• Actively participate in team meetings and technical discussions, and contribute to a culture of continuous improvement and innovation.