In this role, you will oversee the architecture, design, development, and deployment of core software products at Gauss Labs. You will collaborate with a team of talented Software Engineers and AI Scientists while assuming responsibility for the complete software development lifecycle—from individual features to entire products. This position enables you to enhance your expertise in machine learning and AI while contributing to our strategic direction through feature definition, architectural leadership, and implementation of industry best practices.
As a rapidly expanding organization in the industrial AI sector, we are seeking detail-oriented, proactive engineers who methodically identify new opportunities and address complex challenges with precision while upholding rigorous standards for software quality.
At Gauss Labs, we develop sophisticated enterprise-grade ML-based software solutions that address critical challenges for our industry clients. This position requires demonstrated technical proficiency, comprehensive understanding of computer science principles, and proven experience developing reliable, scalable, high-performance systems. Effective teamwork and communication capabilities are crucial for successful collaboration with both technical colleagues and business stakeholders.
Join our Vancouver team and participate in transforming industries through advanced artificial intelligence and machine learning technologies. This role presents exceptional opportunities to work at the forefront of industrial AI, alongside distinguished engineers and scientists, developing solutions that generate substantial business value.

BASIC QUALIFICATIONS

• Bachelor’s degree in Computer Science, Engineering, or related technical field required.
• Minimum of 6 years of professional software development experience, with demonstrated ability to deliver highly scalable, performant, and reliable software solutions.
• Extensive programming expertise (6+ years) in at least one modern programming language such as Python, Java, C/C++, or Rust.
• Advanced proficiency in object-oriented software design principles and development methodologies.
• Demonstrated expertise in resolving complex technical challenges, supported by comprehensive knowledge of data structures, algorithms, and fundamental computer science principles (Operating Systems, Computer Architecture, Databases, Networking).

PREFERRED QUALIFICATIONS

• Advanced degree (Master’s or PhD) in Computer Science or a relevant technical discipline.
• Demonstrated experience across the comprehensive software development lifecycle, encompassing design architecture, implementation, code review processes, test automation, and CI/CD deployment methodologies.
• Substantial background in architecting and implementing large-scale distributed systems within cloud environments.
• Proficient technical expertise with major cloud platforms (AWS, Azure, GCP) and containerization technologies (Kubernetes, Docker).
• Comprehensive understanding of enterprise-grade big data frameworks and technologies (Hadoop, Impala, Spark, Flink, Airflow, Kafka, Redis, MongoDB, Cassandra).
• Practical experience developing and deploying machine learning or AI solutions utilizing industry-standard MLOps tooling (Ray, MLflow, Kubeflow) and data processing libraries (Pandas, Dask).
  [The interview process]
  Application review - Phone interview - Vitual onsite interview - VP interview / Core Value intervie

• Design, develop, and deploy secure, reliable, and scalable ML-based software products that deliver high availability and low latency for enterprise-level customers.
• Own end-to-end development, maintaining high standards in software design, coding, code reviews, automated testing, and deployment within CI/CD practices.
• Participate in code and architectural reviews, and write technical documentation to ensure high code quality and maintainable systems across distributed engineering teams.
• Optimize ML-based software components to fully leverage distributed system architectures, including parallel architectures, clusters, multicore SMPs, and GPUs.
• Work with the SRE team to identify and resolve technical challenges in the production environment.
• Collaborate with AI scientists to integrate algorithmic components into effective solutions and products.
• Partner with project and program managers to understand requirements and effectively address customers’ business challenges.