Technical Staff, Infrastructure

As a Technical Staff member focused on Infrastructure at Reflection, your responsibilities include building and optimizing large-scale AI research infrastructure specifically for reinforcement learning (RL). You will develop scalable training platforms, create efficient inference solutions for various AI models, and work closely with research teams to align infrastructure capabilities with their experimental goals.

To qualify for the Technical Staff, Infrastructure role at Reflection, you should have extensive experience in building high-performance solutions in the AI or software engineering domain. Proficiency in programming languages like C++ or Rust is required, along with a solid understanding of distributed systems, GPU acceleration, and scalable architectures.

Reflection emphasizes collaboration and innovation, offering opportunities for employees to work at the forefront of AI research and infrastructure development. By joining a team with experience in groundbreaking AI projects, you'll gain insights and contribute to advancements using state-of-the-art technology.

As a Technical Staff member at Reflection, you will work on projects that include optimizing large-scale AI research infrastructures and developing efficient production-ready inference solutions. You'll also contribute to the creation of scalable training platforms capable of utilizing extensive GPU resources.

The work environment at Reflection is collaborative and dynamic, characterized by interaction with world-class researchers and engineers in the AI field. You will thrive in an atmosphere that promotes creativity, exploration, and the pursuit of technological advancements in AI systems.

When answering this question, be specific about the projects you've worked on, highlighting how you leveraged programming languages like C++ or Rust to solve complex problems. Discuss the challenges faced and any optimizations or efficiencies achieved in previous infrastructure projects.

Detail your strategic approach to designing scalable architectures, focusing on best practices, tools, and technologies you’ve utilized. Emphasize your understanding of distributed systems and how you ensure performance and reliability in AI applications.

Discuss various optimization techniques you've implemented in previous roles, such as load balancing, memory management, or algorithmic improvements. Be prepared to provide examples of significant performance gains achieved through your optimization strategies.

Provide an instance where you effectively collaborated with research teams, detailing how you identified their needs and aligned infrastructure projects accordingly. Highlight any successful outcomes that resulted from this collaboration.

Identify specific challenges you've faced while developing scalable AI systems, such as managing resources or optimizing performance. Describe your problem-solving methods and the lessons learned from these experiences.

Illustrate your familiarity with GPU acceleration by discussing specific projects where you utilized GPUs. Explain the advantages you’ve experienced with GPU computing in speeding up AI workloads and how it impacts infrastructure design.

Present your understanding of reinforcement learning, emphasizing its unique infrastructure requirements such as real-time data processing and high-throughput computational needs. Share any relevant experience in supporting RL frameworks.

List the monitoring and maintenance tools you've used previously, such as Prometheus or Grafana, highlighting how these tools assist in ensuring the health and performance of AI infrastructure.

Discuss the strategies you employ to ensure reliability in AI production systems, such as failover mechanisms, performance testing, and redundancy measures to prevent system outages.

Articulate your interest in Reflection's mission and how the Technical Staff position aligns with your career goals. Reflect on the equitable opportunity it presents to create impactful AI solutions and high-performance infrastructures.