Backend Developer (Student Position) at Pixellot

Tel Aviv, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

06 Jun, 26

Salary

0.0

Posted On

08 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

NestJS, Python, Node.js, APIs, Microservices, RESTful APIs, Databases, Machine Learning Integration, Code Reviews, System Design, AWS, GCP, Azure, Docker, Kubernetes

Industry

Media Production

Description

Are you a strong backend-oriented student who already builds real systems in production environments? Join Pixellot’s Content team and help power the backend infrastructure behind our AI-driven sports content platform. The team is building the backend layer responsible for generating personalized sports moments, player insights, statistics, and performance visualizations - operationalizing our machine learning models into scalable production services. This is a highly hands-on role focused on building production-grade backend systems, integrating ML components, and working with real production data. At Pixellot, we are dedicated to revolutionizing sports production. Our AI-powered solutions automatically capture sports events and transform them into personalized content, creating engaging experiences for players, teams, and fans. As a Backend Developer, you will directly impact the scalability, reliability, and intelligence of our core content pipelines. As part of your role, you will: Build and maintain backend services using NestJS and Python. Develop and optimize APIs and microservices for sports-related content pipelines. Take product requirements (PRDs), break them down into technical tasks, and implement them end-to-end within an existing codebase. Integrate machine learning models into production systems and work on the services that power model-driven pipelines. Collaborate closely with the AI team to integrate backend systems with machine learning components. Participate in code reviews and contribute to architectural decisions that improve system scalability and reliability. Work with real production data, troubleshoot issues, and continuously improve service performance. Write efficient, production-grade code with strong ownership and attention to system design. Requirements Currently a B.Sc. student in Computer Science or Computer Engineering with at least 1 year remaining in your studies. Availability to work 2 days per week on-site. Proven hands-on backend development experience in real-world production environments, such as professional work experience or technological military service. Strong skills in Node.js (NestJS) and Python. Familiarity with RESTful APIs, databases, and microservice architecture. A modern engineering mindset - leveraging AI tools effectively while maintaining ownership, critical thinking, and production-grade standards. A hands-on, problem-solving mindset with a passion for backend development. Ability to work independently and take ownership over backend tasks end-to-end. Bonus points if you have: Experience with cloud infrastructure (AWS, GCP, or Azure) or containerization tools such as Docker and Kubernetes. Familiarity with machine learning pipelines or experience working alongside AI/ML teams. Why You'll Love Working Here Be part of a pioneering company that is transforming the way fans consume sports content. Work on real, high-impact backend systems used in production environments around the world. Collaborate with talented engineers and AI researchers in a fast-moving, innovative team. Thrive in a culture that values curiosity, ownership, and continuous learning. Join the Team! If you’re excited about building backend systems that power next-generation sports experiences, and want to be part of a passionate, collaborative team shaping the future of sports content - we’d love to meet you!

Responsibilities

The role involves building and maintaining backend services using NestJS and Python to power an AI-driven sports content platform, focusing on generating personalized content, insights, and statistics. Responsibilities include developing and optimizing APIs and microservices, integrating machine learning models into production systems, and ensuring system scalability and reliability.