Senior Site Reliability Engineer at Photoroom
May no longer be active — verify on the employer site
At a glance
- Company
- Photoroom
- Location
- France
- Work arrangement
- On-site
- Employment type
- Full-time
- Experience
- 5–10 years
- Working hours
- 40 hours
- Visa sponsorship
- Offered, according to the listing
- Posted
✨ The Stack take on this Photoroom job
- 🏢 Photoroom has 13 jobs here, 13 with visa sponsorship see all
- 🛂 Visa: Talent passport. EU Blue Card salary: €59,373 a year (1.5 times the average salary) guide
- 💡 No salary shown. Ask Photoroom if it is above the minimum.
Updated 2026-11-05, from our data and official sources.
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About the role
Responsibilities:
- Own and evolve the ML inference infrastructure that serves millions of requests daily across GPU-based systems.
- Design cloud-agnostic, scalable solutions for model serving, load balancing, autoscaling and queuing to meet latency and throughput targets.
- Implement observability and monitoring (metrics, traces, alerts) and use tools like Datadog to maintain platform health.
- Run capacity planning, performance tuning and cost optimisation for GPU workloads and inference clusters.
- Collaborate closely with ML, Product, Web and Mobile teams to identify bottlenecks, improve deployment workflows and enable faster iteration.
- Lead incident response, postmortems and reliability improvements; optionally participate in on-call rotation.
Requirements:
- Significant experience designing and operating large-scale distributed systems with strict availability and latency requirements.
- Hands-on expertise with load balancing, autoscaling, queuing systems, container orchestration (Kubernetes/ECS) and CI/CD pipelines.
- Practical experience running GPU inference workloads and optimising cost/performance for ML deployments.
- Strong monitoring and incident management skills; experience with Datadog or similar observability stacks.
- Familiarity with cloud providers, networking, and security best practices for production services.
- Clear communicator, able to work cross-functionally in a fast-paced startup environment and make pragmatic technical decisions.
Responsibilities
Own and evolve the machine learning inference infrastructure to serve millions of daily AI requests across GPU-based systems. Design cloud-agnostic solutions and manage the full infrastructure lifecycle from architecture to monitoring and optimization.
Requirements
Requires extensive experience designing large-scale distributed systems and operating containerized workloads with high availability. Candidates should be pragmatic, comfortable with independent technical decision-making, and have experience in high-growth startup environments.
Key skills
- Distributed Systems — in 342 other listings on The Stack
- Load Balancing — in 11 other listings on The Stack
- Autoscaling — in 2 other listings on The Stack
- Queuing Systems — in 1 other listing on The Stack
- Traffic Management — in 4 other listings on The Stack
- Low-latency Systems — in 19 other listings on The Stack
- Containerization — in 39 other listings on The Stack
- Infrastructure Architecture — in 3 other listings on The Stack
- Incident Management
- Monitoring
- GPU Workloads
- ML Infrastructure
On The Stack (as of 2026-11-05)
Frequently asked questions about Photoroom
Is this Senior Site Reliability Engineer role still open?
It was posted on 2026-07-07. Listings more than 30 days old may no longer be active, so check Photoroom's site before applying.
Does Photoroom offer visa sponsorship?
This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Photoroom's site.
Where is this role located?
The listing gives the location as France. It is marked on-site.
What skills does this role ask for?
The listing names: Distributed Systems, Load Balancing, Autoscaling, Queuing Systems, Traffic Management, Low-latency Systems, Containerization, Infrastructure Architecture, Incident Management, Monitoring, GPU Workloads, ML Infrastructure.