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Enterprise AI · Toronto / Remote
Who are we?
Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.
We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.
We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.
We are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us!
Why this team?
The GPU Clusters team is at the heart of Cohere's infrastructure, building and operating the superclusters that power our frontier AI models. We're not just managing hardware - we're enabling the research and development that defines what's possible with large language models. This team sits at the intersection of cutting-edge hardware, distributed systems, and AI research, working directly with cloud providers and researchers to solve challenges that few companies in the world are tackling.
As an Engineering Manager here, you'll lead a team of highly motivated engineers who are passionate about GPU infrastructure and AI. You'll be part of a collaborative, remote-first culture that values technical excellence, innovation, and impact. This is a unique opportunity to shape the infrastructure that will power the next generation of AI while working with exceptional technical talent dedicated to advancing the field.
As an Engineering Manager, you will:
Lead and mentor a team of engineers specializing in GPU infrastructure, fostering a culture of technical excellence and continuous improvement
Manage performance, career development, and hiring for team members
Conduct regular 1:1s and team meetings to ensure alignment and address challenges
Provide technical guidance and support to team members on complex infrastructure problems
Define and execute the technical roadmap for GPU cluster deployment, optimization, and scaling
Oversee the implementation of topology-aware scheduling, hardware fault detection, and performance optimization systems
Collaborate with cloud providers to validate and deploy new GPU architectures
Ensure infrastructure reliability, scalability, and security across all GPU environments
Partner with AI researchers to understand emerging infrastructure needs and translate them into robust solutions
Work with the Foundations team on training software stack adaptation for new GPU architectures
Coordinate with Capacity EPM on delivery timelines and resource planning
Interface with Legal and Security teams on compliance requirements
Collaborate with other infrastructure teams on shared goals and dependencies
Establish observability and monitoring frameworks for GPU utilization, performance, and reliability
Implement infrastructure-as-code practices and automation for cluster provisioning
Drive cost optimization initiatives while maintaining performance standards
Manage vendor relationships and contract negotiations for hardware and cloud services
Ensure documentation is comprehensive, up-to-date, and accessible to stakeholders
Experience managing engineering teams with a focus on technical mentorship and growth
Strong communication skills to translate complex technical concepts for diverse audiences
Ability to make data-informed decisions under pressure
Experience working in remote, distributed teams
Commitment to fostering an inclusive and collaborative team culture
Deep expertise in ML/HPC infrastructure: GPU/TPU clusters, distributed training frameworks (JAX, PyTorch, TensorFlow), and high-performance computing environments
Proven experience with Kubernetes at scale: deployment, management, and troubleshooting cloud-native clusters for AI workloads in multi-cloud environments
Knowledge of infrastructure monitoring tools (Prometheus, Grafana)
Familiarity with Terraform, ArgoCD, or other IaC tools
Experience with cost optimization and capacity planning for GPU infrastructure
Track record of collaborating with AI researchers or ML engineers to solve infrastructure challenges
Strong problem-solving abilities with a data-driven approach
Passion for enabling AI research through robust infrastructure
Collaborative mindset with a focus on cross-team success
Willingness to learn and adapt in a fast-paced, evolving environment
A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
Full health and dental benefits, including a separate budget for mental health.
RRSP matching, 401K, Pension Scheme.
100% Parental Leave top-up for up to 6 months, for either parent.
Annual enrichment benefits:
Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
Education & learning stipend for conferences, courses, and coaching.
6 weeks of paid vacation (30 working days!)
Budget for traveling to other offices if you are remote, plus an annual company offsite.
Cohere is remote-friendly. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming soon.
For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
For those not near an office: a co-working benefit so you can work alongside others in your city.
Everyone receives a $500 home office stipend to set up your workspace properly.
If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.
We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.