Machine Learning Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.
Together, we advance your career.
The Person
The individual is expected to work independently, yet in a collaborative fashion, be self-motivated, task orientated and have excellent written and verbal communication skills. You are detail-oriented, you plan, implement, evaluate the work, and strive to improve with every iteration. You have strong analytical and problem-solving skills to understand complex data sets and derive actionable insights.

Key Responsibilities

• Develop, train, and evaluate machine learning models for program complexity categorization and resource forecasting.
• Implement and integrate models into the forecasting system.
• Optimize model performance and ensure accuracy.
• Extract and prepare data for model training and analysis.
• Collect, clean, and analyze data for program complexity estimation and forecasting.
• Develop and implement data analysis methods for determining program complexity drivers.
• Conduct meta-analysis studies to estimate actual work effort based on program complexity.
• Collaborate with cross-functional teams to ensure project success.
• Strong analytical and problem-solving skills to understand complex data sets and derive actionable insights.

Preferred Experience

• 3+ years of combined experience in machine learning model development, data analysis, and statistical modeling.
• Strong understanding of machine learning algorithms, model evaluation metrics, optimization techniques, and statistical analysis techniques.
• Demonstrated ability to implement, validate, and improve supervised and unsupervised learning models.
• Excellent knowledge of Excel, Python, SQL, and database systems.
• Familiarity with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, or related frameworks.
• Experience in evaluation metrics, model selection, and validation techniques such as cross-validation, bootstrapping, and assessing the statistical significance of model improvements.
• Excellent knowledge of data analysis, visualization, and preprocessing techniques. Familiarity with tools such as Pandas, NumPy, and Matplotlib.
• Experience deploying Classical and Modern Time Series Forecasting solutions using tools such as Neural Prophet, SARIMA, Chronos, etc…
• Familiar with cloud-based machine learning platforms such as AWS Sagemaker, Google AI Platform, or Azure Machine Learning for model training, deployment, and monitoring.
• Good understanding of software development principles including version control (e.g. Git), Agile methodologies, and DevOps practices.

Academic Credentials

• Master's degree in Computer Science, Statistics, Data Science, or related field.

LOCATION:
Belgrade, Nis (Hybrid)

Benefits offered are described:
AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.