Software Engineer III

As a Software Engineer III, you will be tasked with designing, developing, deploying, and refining cloud infrastructure and services. You'll work on processing large data sets using statistical algorithms and machine learning methodologies. Additionally, you will lead initiatives to build and optimize machine learning models while collaborating with various teams to enhance the platform's capabilities.

Candidates for the Software Engineer III role should have 4-7 years of relevant experience, a strong technical background in cloud architecture, and proficiency in Microsoft technologies like Azure and SQL Server. A bachelor's degree in Computer Science or a related field is also required, and familiarity with machine learning tools like Keras or TensorFlow is a strong plus.

Collaboration is key for the Software Engineer III role. You will work closely with the Data Infrastructure team to design optimized database schemas and complex queries while also translating product management requirements into actionable data strategies. Working together with peers will enhance the overall machine learning applications for the platform.

For a Software Engineer III with a focus on machine learning, experience with deep learning frameworks, such as Keras and TensorFlow, is beneficial. Proficiency in programming languages like Python and R, along with hands-on experience with statistical algorithms, data mining methodologies, and complex stored procedures, will be important for success in this role.

The salary for a Software Engineer III at this company ranges from $140,000 to $156,000 per year, reflecting the expertise and skill set required for this critical role in our innovative tech environment.

When discussing your experience with cloud architecture, focus on specific projects you've led or contributed to, particularly using Microsoft Azure. Illustrate how you've addressed scalability, security, and performance while deploying applications. Be sure to mention any tools you've used and your role in shaping cloud strategies.

Explain your step-by-step process for building a machine learning model, starting with defining the problem, gathering data, and preprocessing it. Discuss how you select the appropriate algorithms, train the model, and evaluate its performance. Sharing a specific example will demonstrate your practical understanding.

Discuss the methodologies you're familiar with, such as statistical analysis, data mining, or machine learning techniques. Provide examples of projects where you've employed these methodologies effectively, emphasizing the impact of your analyses on decision-making or project outcomes.

Prepare to share a detailed example of a challenging technical issue you encountered, covering the context, your approach to troubleshooting, and the eventual solution. Highlight what resources you used and how you collaborated with your team to resolve it.

Detail your approach to writing high-quality code, including best practices such as code reviews, testing strategies, and performance monitoring. Discuss tools you use for performance optimization and how you incorporate feedback from peers to refine your code.

Articulate your experience working within Agile frameworks, such as Scrum or Kanban. Highlight your roles in sprints, retrospective meetings, and how embracing Agile has improved your team's productivity and collaboration on projects.

Use the STAR method to structure your response, focusing on your leadership skills. Discuss the dynamics of the project, how you motivated your team, and the results achieved under pressure, emphasizing your ability to adapt and guide your team toward success.

Share your strategies for continuous learning, whether through online courses, attending conferences, following thought leaders, or engaging in community discussions. Emphasize specific technologies or frameworks you're currently exploring and how this knowledge enhances your role.

Detail an example where you collaborated with various teams, such as design, product management, or marketing. Discuss the goals of the project, your role, and the final outcome, highlighting how collaboration improved the project's success.

Discuss the specific monitoring and analysis tools you are proficient in, such as Azure Monitor, SQL Profiler, or other third-party applications. Provide examples of how you have used these tools to identify performance bottlenecks or improve application uptime.