AI Infrastructure Engineer

About the role

Fin is on the lookout for experienced engineers to enhance the systems that underpin our proprietary AI models. This role involves joining a dedicated team that focuses on the entire spectrum of AI infrastructure, from optimizing GPU performance to deploying advanced models such as Fin Apex. You will play a crucial part in shaping the future of our AI capabilities.

Key facts

What you'll do

• Architect and scale distributed training pipelines specifically designed for large language models (LLMs) and transformer architectures, encompassing everything from data preprocessing to final model evaluation.
• Develop and sustain inference services that emphasize low latency and high reliability, incorporating features such as autoscaling and effective traffic management.
• Enhance GPU performance by diagnosing performance bottlenecks and fine-tuning computational kernels for optimal efficiency.
• Collaborate closely with machine learning scientists to transition experimental training and inference methodologies into robust production systems.
• Provide mentorship to team members, fostering a culture of operational excellence and ensuring high standards of system reliability.
• Engage in code reviews and contribute to best practices in software development within the team.
• Participate in the design and implementation of monitoring and alerting systems to ensure the health and performance of AI infrastructure.
• Stay updated with the latest advancements in AI and machine learning technologies, integrating relevant innovations into existing systems.

Requirements

• A minimum of 5 years of experience in software engineering, demonstrating a strong technical foundation.
• A degree in Computer Science, Computer Engineering, or a related field, or equivalent hands-on experience in the industry.
• Proficiency in at least one programming language, such as Python, Ruby, Java, or Go, with a solid understanding of software development principles.
• Practical experience in at least one of the following areas: training models for transformers or LLMs, executing model inference at scale, or low-level GPU programming using CUDA or Triton.
• Familiarity with production environments, particularly those that operate at scale, is essential.
• Strong problem-solving skills and the ability to work collaboratively in a team-oriented environment.

Nice to have

• Experience working at AI-focused companies that handle their own model training or inference processes.
• Knowledge of Kubernetes for workload management and orchestration.
• Familiarity with cloud services, particularly AWS or other leading cloud platforms.
• Experience using Python for infrastructure or machine learning tasks in a production setting.
• Involvement in open-source projects, personal technical endeavors, or contributions to published materials in the field.

Skills & tools

• Expertise in transformers and large language models (LLMs)
• Proficient in CUDA and Triton for GPU programming
• Programming skills in Python, Ruby, Java, or Go
• Experience with Kubernetes for container orchestration
• Familiarity with AWS or similar cloud services
• Knowledge of distributed training and inference systems

Practical notes

• The compensation package includes a competitive salary along with equity options.
• Benefits encompass a pension plan with a matching contribution of up to 4%, comprehensive health and dental coverage, life insurance, and a cycle-to-work initiative.
• Flexible paid time off is available to support work-life balance.
• Parental leave benefits include paid maternity leave and six weeks of paid paternity leave.
• Daily lunches, snacks, and a fully stocked kitchen are provided to employees.
• Each employee is equipped with a MacBook, with Windows options available for specific roles.
• Access to Claude Code and other AI tools is provided to encourage experimentation and innovation.

Join us at Fin and be part of a dynamic team that is at the forefront of AI infrastructure development. Your expertise will help drive the evolution of our AI capabilities, making a significant impact on our products and services. If you are passionate about AI and eager to work in a collaborative environment, we would love to hear from you.