Original listing text, shown exactly as published by the company.
About the role
- You’ll own the Machine Learning Inference Infrastructure at Photoroom, powering millions of AI requests every day across GPU-based systems.
- You’ll be responsible for the infrastructure that deploys and runs machine learning workloads, partnering closely with ML engineers to ensure services remain reliable, scalable and cost-efficient.
- You’ll design and build cloud-agnostic infrastructure solutions that support both current and future AI workloads. Requests can range from milliseconds to several seconds and process payloads of tens of megabytes.
- You’ll work across the full infrastructure lifecycle, from architecture and implementation through to monitoring, optimisation and incident management, using tools such as Datadog to maintain system health.
- You’ll build and improve systems for load balancing, autoscaling, queuing and workload orchestration, ensuring consistent performance under rapidly growing demand.
- You’ll work directly with engineers across ML, Product, Mobile and Web teams to identify bottlenecks, improve deployment workflows and enable faster iteration.
- You’ll monitor production systems, analyse usage patterns and make infrastructure decisions based on real-world performance and user impact.
- Optional: Participate in the team’s on-call rotation to help maintain platform reliability.
About you
- You have experience designing and operating large-scale distributed systems with high availability and reliability requirements.
- You have hands-on experience with load balancing, autoscaling, queuing systems and traffic management at scale.
- You have worked on low-latency, real-time backend systems and understand how to optimise for performance and throughput.
- You have experience building resilient, redundant infrastructure capable of handling failures gracefully.
- You have designed and operated platforms that deploy and run containerised workloads at high scale while maintaining an excellent developer experience.
- You have experience supporting workloads that vary significantly in duration, from milliseconds to several seconds.
- You are highly pragmatic and focus on delivering business impact quickly, leveraging existing tools and frameworks where appropriate rather than reinventing solutions.
- You demonstrate strong ownership and are comfortable making technical decisions independently while collaborating effectively across teams.
- You have previously worked in a high-growth startup or similarly fast-moving environment.
- You enjoy learning from others, sharing knowledge and contributing to a collaborative engineering culture.
- Experience supporting machine learning infrastructure or GPU workloads is a plus, but not required.
- You are fluent in English (French is not required).
If you think you have what it takes but don’t meet every single point above, please still apply. We’d love to chat and see if you could be a great fit.
Hiring Process
- Screening call with the Talent Manager
- Technical interview with the Hiring Manager
- Home technical assignment, followed by a review with the team (can be compensated and under 3 hours)
- Culture fit interviews and meet the team
- Reference check & Offer
Support – If you have a medical condition or an individual need for an adjustment to our process, and you believe this may affect your ability to be at your best, please let us know so we can discuss how we can support you and make any adjustments that may be needed.
