Architect designing enterprise - grade AI/ML architectures for Quantiphi. Leading AI applications and ML strategy with a focus on scalability, security, and integration.
About the role
- Software Engineer for ML Infrastructure at Slack, architecting systems to support large scale AI deployment and reliability. Engage in deep systems engineering focusing on ML lifecycle and infrastructure scalability.
Responsibilities
- Design, build, and operate systems to train, serve, and deploy machine learning models at scale, with a focus on reliability, performance, and operational simplicity
- Evolve GPU backed inference infrastructure to support high throughput, latency sensitive workloads, including large scale model serving
- Architect and optimize distributed training and data processing systems using platforms such as Ray, Airflow, Spark, or similar technologies
- Build and maintain Kubernetes based platforms and orchestration layers using tools such as KubeRay, vLLM, and internally developed services
- Architect solutions that bridge legacy systems with modern technologies while maintaining monolithic application stability
- Develop robust monitoring, observability, and alerting for production ML workloads to ensure operational excellence
- Partner closely with AI Platform, ML modeling, security, and product engineering teams to design infrastructure that supports evolving AI use cases
- Provide technical leadership through design reviews, mentorship, and by setting engineering standards and long term architectural direction for ML infrastructure
- Author technical design and architecture documentation, and contribute thought leadership through engineering blog posts
Requirements
- Significant professional experience in software engineering with a strong focus on infrastructure, backend systems, platform engineering, or MLOps
- Deep experience building and operating distributed systems, including expert level knowledge of Kubernetes and container based platforms
- Hands on experience with modern ML infrastructure and serving stacks such as Ray or KubeRay, vLLM, or similar training and inference orchestration frameworks
- Experience working with GPU infrastructure, including performance optimization and operational management at scale
- Strong experience with data infrastructure and orchestration technologies such as Airflow, Spark, or similar systems
- Experience building and operating cloud native systems on public cloud platforms such as AWS, GCP, or Azure, including infrastructure as code
- A demonstrated ability to drive technical direction for complex systems and balance short term delivery with long term architectural goals
- Excellent written communication, as well as ability to thrive in an asynchronous and globally distributed infrastructure team.
- A related technical degree required
Benefits
- time off programs
- medical
- dental
- vision
- mental health support
- paid parental leave
- life and disability insurance
- 401(k)
- employee stock purchasing program
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