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.
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Responsibilities
- 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.
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).
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[The interview process]
Application review - Phone interview - Virtual onsite interview - VP interview /
Core Value interview
