FAQs
What is the primary focus of the Software Development Engineer role at Adobe?
The primary focus is to design and implement scalable machine learning solutions and integrate them into robust backend systems within the Adobe Learning Manager eLearning platform.
What qualifications are required to apply for this position?
A Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field is required, along with 4-8 years of hands-on experience in relevant areas like Recommendation Systems and Natural Language Processing.
What programming languages and technologies should candidates be proficient in?
Candidates should have strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, and scikit-learn, as well as experience with backend technologies including Java, Spring, and RESTful APIs.
What are some of the responsibilities associated with this role?
Responsibilities include owning product solutions through their end-to-end development lifecycle, engaging with customers to understand needs, crafting use cases, and mentoring junior developers.
Is experience with cloud platforms a necessity for this role?
While it is not a strict requirement, experience with cloud platforms like AWS or Azure and their ML services is considered a "good to have" skill.
Will the Software Development Engineer be involved in mentoring other developers?
Yes, the role includes mentoring junior developers to maximize team potential.
What soft skills are important for this position?
Strong communication skills, excellent problem-solving abilities, and the capacity to influence both technical and non-technical stakeholders are crucial for this role.
What is the company's stance on diversity and inclusion?
Adobe is committed to being an Equal Employment Opportunity and affirmative action employer, promoting diversity and inclusion throughout the organization.
How does this role contribute to customer experience?
The Software Development Engineer will lead the creation of advanced SaaS applications that leverage machine learning to enhance user experiences and improve operational efficiency.
Are there any specific methodologies that the candidate should be familiar with?
Familiarity with MLOps practices and tools for model deployment and monitoring is favorable, as well as knowledge of containerization and orchestration technologies.