Machine Learning Engineer; Hybrid- Greenfield

Position Overview

Position: Machine Learning Engineer (Hybrid – Greenfield Opportunity). Job Title: AI/ML Engineer – Greenfield AI Project. Location: Irvine, CA (onsite). Monday through Thursday onsite, Fridays remote. Compensation: $75-95 an hour on a 2-year contract that will convert to full-time. UNABLE TO OFFER SPONSORSHIP – MUST BE US CITIZEN/ GREEN CARD HOLDER. About Us: We are on a mission to develop innovative AI solutions that will revolutionize our workforce. As we embark on an exciting new greenfield AI project, we are seeking an exceptional AI/ML Engineer to join our team and lead the development of machine learning models that solve real-world business challenges.

Key Responsibilities

• Collaborate with data scientists and stakeholders to translate project goals into scalable ML solutions.
• Design, develop, and train models using state‑of‑the‑art machine learning techniques and tools.
• Select appropriate annotated datasets and transform raw data into machine learning‑ready formats.
• Analyze and process structured/unstructured data for training and evaluation.
• Develop feature extraction and selection pipelines to improve model performance.
• Run controlled experiments and perform statistical analysis to validate models.
• Refine model hyperparameters and evaluation metrics for optimal performance.
• Work closely with ML Ops to deploy and monitor models in production environments.
• Ensure all models are integrated seamlessly into existing systems.
• Participate in code reviews, pair programming, and knowledge‑sharing sessions.
• Write testable, production‑quality code that aligns with engineering best practices.

Required Qualifications

• 3–5 years as an ML/AI Engineer or 1–3 years in an ML/AI leadership role.
• Proven experience building and deploying machine learning models in production.
• Solid understanding of classical ML algorithms (classification, regression, clustering).
• Experience working with changing datasets and real‑time data pipelines.
• Hands‑on experience with Python and frameworks like PyTorch, TensorFlow, Scikit‑learn.
• Strong knowledge of data processing (ETL), feature engineering, and statistical evaluation.
• Solid understanding of REST APIs, CI/CD, and containerized deployments (Docker, Kubernetes).
• Strong communication, analytical thinking, and problem‑solving skills.
• Education: Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related quantitative field.

Preferred Qualifications

• Master’s or PhD degree in Computer Science, Engineering, or a related field.
• Experience with neural networks and deep learning applications in computer vision, time‑series analysis, or reinforcement learning.
• Familiarity with MLOps tools (MLflow, Kubeflow, Sage Maker, etc.).
• Exposure to cloud platforms (AWS, GCP, Azure).
• Familiarity with version control and experimentation tracking tools.
• Basic knowledge of data governance, security, and compliance standards.