Mid Level Backend Engineer Streaming Ai

Mid-Level Backend Engineer (Streaming & AI) at BuiltInSeattle — Research Report

Introduction

The role of a Mid-Level Backend Engineer (Streaming & AI) is one of the most dynamic opportunities in the current tech landscape. It sits at the intersection of high-performance data streaming and cutting-edge artificial intelligence, making it invaluable for companies building next-generation platforms. Whether you are looking for a BuiltInSeattle internship or a full-time position, the deadline is flexible as new jobs are added daily. This career path offers exceptional growth, the chance to work on complex systems, and the potential to shape how AI interacts with real-time data streams.

Overview of BuiltInSeattle

BuiltInSeattle is not your typical company; it is a powerhouse that drives innovation for brands across the globe. The company specializes in creating robust backend infrastructure that supports massive streaming volumes and integrates advanced AI models seamlessly. In the industry, BuiltInSeattle competes with top-tier firms but maintains a unique niche by focusing on the synergy between streaming efficiency and machine learning capabilities. Their key products include real-time data pipelines, AI-driven analytics dashboards, and scalable cloud architectures that handle petabytes of information.

The company’s market presence is growing rapidly, with a reputation for technical excellence and a culture that prioritizes curiosity over perfection. BuiltInSeattle fosters a work environment where engineers are encouraged to experiment, learn, and push boundaries. People want to work here because they are given the autonomy to solve real-world problems and the resources to do so effectively. The culture is collaborative, inclusive, and deeply committed to professional development, making it a top choice for ambitious engineers.

Mid-Level Backend Engineer (Streaming & AI) Role

Role Overview

As a Mid-Level Backend Engineer, you will be responsible for designing, building, and maintaining the core systems that power streaming and AI functionalities. Your day-to-day work involves optimizing data pipelines, implementing AI algorithms, and ensuring system reliability under high load. The impact of your work is direct and significant; you will enable the product to deliver real-time insights and seamless user experiences. This role is critical for the business, as it ensures that the backend infrastructure can scale to meet growing demands while maintaining performance.

Detailed Responsibilities

Your responsibilities will cover a wide range of technical tasks essential for the role:

• Design and optimize high-throughput data streaming pipelines using tools like Apache Kafka or AWS Kinesis.
• Implement and deploy machine learning models for real-time data processing and analytics.
• Develop scalable backend APIs that support both streaming and AI features.
• Monitor system performance and troubleshoot issues to ensure 99.9% uptime.
• Collaborate with data scientists and frontend engineers to integrate AI capabilities into the user interface.
• Write clean, maintainable, and well-documented code following industry best practices.
• Participate in code reviews and contribute to the continuous improvement of the engineering team.

Day-to-Day Workflow

A typical day starts with a team stand-up to discuss progress and blockers. You will then dive into coding, focusing on optimizing a specific data pipeline or refining an AI model. Mid-morning, you might attend a technical deep-dive session to learn about a new streaming technology or AI framework. After lunch, you will likely spend time testing your code, running performance benchmarks, and addressing any bugs. The afternoon often involves collaboration with other teams to align on integration requirements. By the end of the day, you will document your progress and plan tasks for the next day.

Tools and Technologies

The engineering stack at BuiltInSeattle is modern and robust, designed to handle complex workloads. You will primarily work with Python and Go for backend development, leveraging libraries like TensorFlow and PyTorch for AI tasks. For streaming, the team uses Apache Kafka, AWS Kinesis, and Redis for caching. The infrastructure is built on Kubernetes and Docker, deployed via AWS or Google Cloud. You will also utilize monitoring tools like Prometheus and Grafana to track system health. Database management involves PostgreSQL and MongoDB, ensuring flexible and scalable data storage.

Skills and Requirements

Technical Skills

To succeed in this role, you must possess a strong foundation in specific technical domains. Proficiency in Python and Go is essential, along with a deep understanding of data structures and algorithms. You should have experience with distributed systems, message queues, and cloud infrastructure. Knowledge of machine learning concepts, including model training, deployment, and optimization, is critical. Familiarity with streaming technologies like Kafka and Kinesis is required, as well as experience with containerization and orchestration tools like Docker and Kubernetes.

Soft Skills

Beyond technical expertise, soft skills are vital for thriving in the BuiltInSeattle environment. Strong communication skills are necessary to explain complex technical concepts to non-technical stakeholders. Teamwork is essential, as you will collaborate closely with data scientists, frontend engineers, and product managers. Problem-solving abilities are crucial for tackling unexpected challenges and optimizing system performance. Adaptability is also key, given the fast-paced nature of the tech industry and the constant evolution of AI and streaming technologies.

Experience Expectations

BuiltInSeattle targets motivated candidates with relevant experience, typically 2–4 years in backend engineering. A portfolio showcasing projects in streaming or AI is highly valued, as it demonstrates practical application of your skills. While a graduate degree is not required, a strong academic background in computer science or a related field is beneficial. GPA expectations are flexible, but a track record of academic excellence can help. The company prioritizes curiosity and initiative over perfection, so candidates who show a willingness to learn and grow are preferred.

Salary and Benefits

The salary for a Mid-Level Backend Engineer (Streaming & AI) at BuiltInSeattle is competitive, reflecting the high demand for these skills. The estimated pay range is between $120,000 and $160,000 annually, depending on experience and location. In addition to base salary, the company offers a comprehensive benefits package. Perks include a remote or hybrid work model, a learning and development budget, and access to cutting-edge tools and technologies. There are also opportunities for full-time conversion if you start as an intern, along with performance-based bonuses and equity options.

BuiltInSeattle Hiring Process

Step-by-Step Hiring Stages

The hiring process at BuiltInSeattle is structured to ensure a thorough evaluation of your skills and fit. The stages include:

1. Application: Submit your resume, portfolio, and a brief statement of interest.
2. Screening: Your application is reviewed by the ATS and a human recruiter for key qualifications.
3. Assignment: Complete a technical project focused on streaming or AI to demonstrate your capabilities.
4. Interviews: Participate in one or more rounds of interviews, including technical and behavioral assessments.
5. Offer: Receive a formal job offer if you successfully pass all stages.

Application Timeline

You can apply anytime, as new jobs are added daily. The typical process duration is 2–4 weeks, from application to offer. It is advisable to apply early to secure a spot, as the number of qualified candidates can be high. The timeline may vary slightly depending on the specific role and the urgency of the hiring team.

Screening Methods

BuiltInSeattle uses an automated tracking system (ATS) to screen applications for key keywords related to streaming, AI, and backend engineering. Your portfolio is also reviewed to ensure it includes relevant projects. Recruiters look for evidence of practical experience, problem-solving skills, and a passion for technology. A strong resume that highlights your achievements and technical skills is essential for passing the screening stage.

Interview Preparation

Example Interview Questions

During the interview, you may encounter questions such as:

• How would you design a real-time data streaming pipeline to handle high throughput?
• Explain the process of deploying a machine learning model for real-time inference.
• What strategies would you use to optimize the performance of a backend API under heavy load?
• How do you ensure data consistency in a distributed system?

How to Answer

When answering these questions, use the STAR method (Situation, Task, Action, Result) to structure your responses. Provide specific examples from your past experience to demonstrate your skills. Be clear and concise, focusing on the technical details and the impact of your actions. Show your problem-solving process and how you approach challenges systematically.

What Recruiters Evaluate

Recruiters evaluate your technical proficiency, problem-solving abilities, and cultural fit. They look for candidates who can demonstrate a deep understanding of streaming and AI concepts, as well as the ability to apply them in real-world scenarios. Communication skills, teamwork, and adaptability are also key criteria. The goal is to find engineers who are not only technically skilled but also passionate about innovation and continuous learning.

How to Get Selected

Practical Tips

To increase your chances of getting selected, tailor your application to highlight your experience with streaming and AI. Include a strong portfolio with relevant projects that showcase your technical skills. Practice coding problems and system design questions to prepare for the technical interview. Be proactive in your communication, showing enthusiasm for the role and the company. Finally, network with current employees to gain insights into the company culture and hiring process.

Common Mistakes to Avoid

Candidates often get rejected for failing to demonstrate practical experience with streaming or AI technologies. A generic resume that does not highlight relevant skills is another common mistake. Poor communication during the interview, such as being vague or unprepared, can also lead to rejection. Avoid focusing too much on theory without providing real-world examples. Lastly, failing to show curiosity and a willingness to learn can be a disadvantage.

How to Stand Out

To stand out, create a portfolio that includes innovative projects in streaming or AI, demonstrating your ability to solve complex problems. Network with current employees to gain insider insights and show your genuine interest in the company. Tailor your application to align with the specific needs of the role, highlighting your unique skills and experiences. Be proactive in your communication, asking thoughtful questions and showing enthusiasm for the opportunity. Finally, demonstrate your adaptability and passion for continuous learning, which are highly valued at BuiltInSeattle.

Final Thoughts

The opportunity to become a Mid-Level Backend Engineer (Streaming & AI) at BuiltInSeattle is a chance to shape the future of technology. With a competitive salary, a supportive culture, and the resources to innovate, this role offers everything an ambitious engineer needs to thrive. If you are ready to take on complex challenges and make a real impact, apply now and start your journey with BuiltInSeattle. The deadline is flexible, as new jobs are added daily, so don’t wait to secure your spot.

Frequently Asked Questions

Q: What is the salary for Mid-Level Backend Engineer (Streaming & AI) at BuiltInSeattle?
A: The estimated salary range is between $120,000 and $160,000 annually, depending on experience and location.

Q: How competitive is it to get hired at BuiltInSeattle?
A: The process is competitive, but BuiltInSeattle prioritizes curiosity and initiative over perfection, so candidates with relevant experience and a strong portfolio have a good chance.

Q: What skills are most important for this role?
A: Proficiency in Python and Go, experience with streaming technologies like Kafka, and knowledge of machine learning concepts are the most critical skills for this role.