Associate Machine Learning Engineer

Accountabilities:: Collaborate with Machine Learning Engineers, Data Scientists, Data Engineers, DevOps specialists, and other technical teams to understand requirements and support the development of machine learning solutions aligned with business objectives. Assist in building, testing, maintaining, and improving software components and infrastructure that support machine learning applications. Contribute to the development and maintenance of automated data pipelines, machine learning workflows, and model deployment processes. Support cloud-based machine learning solutions using technologies such as AWS, Databricks, and Apache Spark. Participate in continuous integration and continuous delivery (CI/CD) processes, helping improve automation, deployment reliability, and operational efficiency. Monitor machine learning applications, investigate technical issues, and collaborate with senior engineers to identify and implement solutions. Help maintain scalable infrastructure and tooling that enable AI and machine learning capabilities across the organization. Contribute to technical discussions, cross-functional meetings, and project planning by sharing feedback, asking relevant questions, and helping clarify requirements. Document technical solutions, development processes, and lessons learned to facilitate knowledge sharing and improve team practices. Follow established software engineering, machine learning, security, data governance, and development standards. Continuously strengthen technical expertise through mentorship, training, experimentation, and hands-on project experience. Requirements: Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Machine Learning, or another related quantitative discipline. 0–2 years of experience in machine learning engineering, software engineering, data engineering, or a related technical field. Internship experience, academic research, capstone projects, or equivalent practical experience in artificial intelligence, machine learning, or software development is preferred. Experience using Python through academic projects, internships, research, or professional work. Foundational understanding of machine learning concepts, algorithms, model development, and deployment processes. Familiarity with software engineering fundamentals, including version control, software testing, debugging, and code review practices. Basic understanding of data engineering principles, data structures, and automated pipeline development. Exposure to cloud computing platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP) is an advantage. Strong analytical thinking and problem-solving abilities, with the capacity to investigate technical challenges and develop practical solutions. Excellent communication and collaboration skills, including the ability to work effectively with both technical and non-technical stakeholders. Demonstrated curiosity, adaptability, and enthusiasm for learning emerging technologies and improving engineering practices. Ability to manage assigned tasks independently while recognizing when guidance or support from senior colleagues is needed. Interest in how machine learning infrastructure and AI-driven solutions can improve customer experiences and business outcomes. Preferred Qualifications: Academic or professional experience in human resources, social sciences, or psychology. Contributions to open-source projects, particularly those involving Python. Demonstrated interest in emerging machine learning technologies, AI infrastructure, and modern engineering practices. Motivation to explore innovative tools and approaches that improve the scalability, reliability, and performance of machine learning solutions. Benefits: Competitive annual base salary ranging from $106,900 to $152,700 , depending on experience, qualifications, and work location. Comprehensive employee benefits package. Fully remote working arrangement within the United States, with no regular office attendance required. Opportunities to work with modern machine learning technologies and cloud platforms. Hands-on experience developing AI and machine learning infrastructure at scale. Mentorship and collaboration with experienced engineering professionals. Professional development opportunities through training and practical project experience. Collaborative and inclusive working environment. Opportunities to contribute to innovative AI-powered products and customer-focused solutions.