S

Scality

Cyber-resilient cloud object storage software

Platform Security Engineer

Full-TimeUpdated on 10/23/2026
No salary listed
Mid
Paris, France
In PersonOnsite in Paris.
CybersecuritySecurity Engineering & Architecture

About the job

Requirements
  • Linux is the candidate’s primary operating system, and they are comfortable working in the terminal and using a shell.
  • Approximately 2–4 years of relevant experience, or a shorter track record supported by unusually strong evidence of the listed skills.
  • Working ability in Python and Bash, including writing and maintaining code in a production codebase.
  • A demonstrable interest in security, evidenced through activities such as CTFs, a home lab, reading advisories and CVE writeups, a hardening project, security-adjacent contributions, or a blog.
  • Comfort using Git in a collaborative environment.
  • Ability to read and understand existing code before modifying it.
  • Judgment to distinguish real risk from a scanner’s opinion and to check whether vulnerable code is reachable before reporting exposure.
  • Willingness to state plainly when an answer is uncertain rather than provide an indefensible answer.
  • French language ability for day-to-day team work.
  • Written communication skills in English.
Responsibilities
  • Maintain a standing exposure record for each shipped component and release, documenting SBOM contents, upstream vendor decisions, and whether vulnerable code paths are reachable in the configured product.
  • Continuously detect CVEs across repositories, vendored third-party modules, container images, and shipped RPMs.
  • Integrate CVE detection with SBOM tooling and GitHub Actions pipelines so exposure is computed during release builds.
  • Define an interface with security engineers and customer-facing teams that establishes who produces technical assessments, who owns customer-facing wording, and how both meet customer security policy response commitments.
  • Store assessments durably in a queryable system rather than only in ticket comments.
  • Establish a remediation loop with clear ownership and response targets, integrated into Jira workflows.
  • Use AI-assisted remediation where appropriate, from advisory through proposed patch to tested build, with a human deciding what ships.
  • Drive platform-level hardening of SCOS, including a hardened default configuration for the distribution.
  • Advance SELinux enforcement across RING and S3C.
  • Improve supply-chain integrity through artifact provenance, signing, dependency currency, and reproducible and verifiable builds.
  • Improve secrets and credential handling across the delivery pipeline.
  • Advance cryptographic posture, including FIPS 140-3 readiness.
  • Translate supply-chain security expectations under NIS2 and the EU Cyber Resilience Act into automation and evidence.
  • Contribute to platform threat modelling, customer hardening guidance, and security reviews of significant design changes.
Desired Qualifications
  • Experience with CVE triage, CVSS scoring, or vulnerability management.
  • Ability to read vendor advisories and understand vendor triage, including NVD, RHSA, Red Hat severity, and will-not-fix decisions.
  • Experience with supply-chain security tools, including SBOM formats such as SPDX and CycloneDX, scanners such as Trivy, Grype, and Syft, and artifact signing.
  • Experience with RPM packaging or Linux distribution internals.
  • Experience with SELinux policy work.
  • Experience with CI/CD systems, especially GitHub Actions.
  • Experience with RHEL or Rocky Linux versions 8 and 9.
  • Experience with containers, Docker, containerd, or systemd units.
  • Experience with Ansible or Salt.
  • Awareness of NIS2, the Cyber Resilience Act, or FIPS 140-3.
  • Familiarity with S3 or object storage concepts.
  • Serious use of AI coding tools in a real workflow, such as Claude Code or an equivalent beyond autocomplete.

About the company

Scality provides cyber-resilient storage software for hybrid, private, and public clouds. Its flagship products, Scality RING and ARTESCA, manage, access, and protect backup data across cloud environments by using software-defined storage that can run in multiple infrastructures. This makes it possible for customers to choose their preferred cloud setup while keeping data safe and easy to access. Scality differentiates itself through its focus on data protection and performance in object storage for backups, along with offering integration services to build complete data solutions. The company is recognized for being a leader in backup object storage and collaborates with ecosystem partners to deliver end-to-end storage solutions. The goal is to give organizations the freedom to select the cloud infrastructure that fits their needs while ensuring their data is protected, readily accessible, and efficiently managed.

Company Size

201-500

Company Stage

Series E

Total Funding

$175.4M

Headquarters

San Francisco, California

Founded

2009

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Simplify's Take

What believers are saying

  • September 9, 2026 Maestro reached general availability for ARTESCA backup-as-a-service providers.
  • July 2026 WEKA France support and June 2026 OVHcloud broaden European sales channels.
  • June 2, 2026 Biomemory partnership opens archival DNA storage for regulated data.

What critics are saying

  • WEKA and OVHcloud partner on adjacent infrastructure, compressing Scality's differentiation by 2027.
  • Maestro is on-premises only today; SaaS delay limits managed-service adoption and revenue expansion.
  • If hyperscalers and integrated storage appliances absorb sovereignty features, Scality becomes a niche control plane.

What makes Scality unique

  • RING and ARTESCA span petabyte-to-exabyte storage, now anchored by Maestro for distributed fleets.
  • Scality's June 2026 OVHcloud deal sells sovereign, GPU-direct storage without U.S. hyperscaler dependence.
  • Scality's February 2026 WEKA tiering pairs flash-speed AI with cheaper object retention.

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Benefits

  • Global offices & international relocation
  • Sports allowance program
  • Flexible spending account
  • Sponsor industry events, educational conferences, & technical seminars
  • Donation matching

Growth & Insights and Company News

6 month growth

0%

1 year growth

0%

2 year growth

0%
Hack Ware News
Oct 9th, 2026
Scality entrusts the contextual memory storage of AI agents, without performance guarantees.

Scality entrusts the contextual memory storage of AI agents, without performance guarantees. Avery Calloway - 10/09/2026 - 6 minutes of reading Scality Unveils AI Inference Factory at Scality Day in Paris Scality has announced its latest innovation, the AI Inference Factory, during Scality Day in Paris - a software stack designed for on-premises inference that offloads the contextual memory of AI models to storage. This move aims to address the limitations of GPU memory for AI agents as the complexity of context expands during operations. Although Scality has launched this product with a fixed budget model in mind, it has not yet guaranteed performance outcomes. AI models and reasoning agents require context retention throughout a session, which can grow increasingly cumbersome. Scality's announcement, made on October 8, aims to tackle these demands, promoting the use of AI Inference Factory on enterprise infrastructure instead of relying on cloud providers. This software stack integrates validated open-weight models, an inference service, a control layer, and Scality's own Scality AI Data Infrastructure (ADI). The target market includes businesses, government entities, and new cloud service providers focused on AI. Scality offers this stack through both licensed and fully managed service options, delivering a complete solution that it validates, deploys, and maintains. Shared KV cache for agents' context. To produce each token, the AI model utilizes a Key-Value (KV) cache, storing previous computations related to the context. This technical memory mechanism enables AI agents and large language models to respond swiftly and cost-effectively during complex tasks or conversations. Instead of revisiting and recalculating entire conversation histories for every word generated, the system retains prior attention states directly within the graphics card memory (VRAM). According to Scality, reasoning models and agent loads, particularly during long sessions, are the most impacted. The company has opted to move the KV cache from GPU servers to ADI, marketed as a shared cache with multi-petabyte capacity. Furthermore, the architecture separates the two phases of inference: a group of GPUs first reads the request and updates the cache (prefill), while a second group then accesses this cache to generate tokens (decode). "With ADI as a shared cache, any decode GPU can retrieve any context without size limitations," explains Giorgio Regni, Scality's Chief Technology Officer. It's important to note that this KV cache does not overlap with an agent's memory from one session to another. Instead, it maintains the context of the ongoing session, including instructions, read documents, tool results, and reasoning already produced. Retention of historical data, preferences, and organizational knowledge requires different mechanisms, such as retrieval-augmented generation or long-term memory, which are not part of this announcement. This distinction is crucial for agent functionality, allowing an agent that waits for tool feedback or human validation to resume tasks without the GPU recalculating all accumulated context. Hence, this development addresses the costs associated with extended and interrupted sessions, not the learning capabilities of agents. ADI operates on a scale of multiple petabytes, using NVMe or HDD disks connected to GPUs via RDMA. Data is distributed across flash memory, disks, and optionally tape. Laboratory performance insights from Scality. The performance numbers shared stem from Scality's internal tests. The Gemma-3 27B model loads in 1.9 seconds - nearly ten times faster than loading from a local NVMe drive. Restoring a context of 14,000 tokens from ADI takes 166 milliseconds before producing the first token, which is only 83 milliseconds longer than loading from HBM. Accessing cached data is 14 times faster than recalculating it for the same context, going up to 72 times faster for 439,000 tokens. The cache exceeds GPU memory capabilities by over 80 times and can restore 1,000 sessions without recalculation. According to the company, latency between generating tokens remains steady, with context restored before the first token is emitted. However, these tests are based on a modest setup: a server with eight GPUs, five storage servers, and four RDMA links operating at 100 Gbit/s. Scality has not released any service-level commitments. Its control layer effectively distributes requests according to service-level agreement (SLA) targets, yet these are ultimately determined by the client. The company cites two external studies. DistServe, presented at OSDI 2024, reported a 7.4 times increase in requests served at similar latencies thanks to the separation of phases, while Moonshot AI noted a 75% increase in requests processed with its Mooncake shared cache. Fixed budget per token amidst unpublished pricing. Scality's second key argument revolves around costs. In cloud environments, providers charge per token generated, with bills escalating alongside usage. Additionally, a provider may alter a model's version, risking disruption to dependent applications. By deploying on-premise, enterprises can maintain control over model versions and pay for infrastructure rather than consumption. The stack supports various models, including Mistral, Gemma, gpt-oss, Qwen, Kimi, GLM, and DeepSeek, compatible with Dell, HPE, Lenovo, and Supermicro servers. This variety addresses common needs within the market. Yet, Scality has not provided pricing details or client references. On-premise deployment incurs costs for GPU purchase, energy, and operational maintenance. In the cloud, token prices are competitive. For example, as of October 8, Anthropic charges $0.10 per million tokens for its Claude Haiku 5.5, and Google Cloud has set spending caps for computational loads. On-premise solutions excel in scenarios of massive, consistent usage, while cloud services are advantageous when usage is low or variable. Thus, cost savings will likely vary by case until Scality reveals its pricing. IDC Vice President Nataliya Yezhkova points out the market dynamics, stating that on-site inference is now a viable option "as long as the infrastructure can efficiently preserve and serve model states at scale." Scality isn't pioneering this field alone; Red Hat has also entered the arena with its AI Inference Server since May 2025, supporting a distribution of the vLLM engine, alongside the distributed inference project llm-d. A response to architectural challenges yet to be proven. The distinction between prefill and decode, as measured by DistServe, and the reuse of the KV cache are established practices that Scality hasn't invented. Fundamentally, the approach makes sense. The differentiating factor lies in how it handles inference state storage, as opposed to the engine work emphasized by Red Hat. In this paradigm shift, agent context transitions from a computational burden to a stored data format, allowing GPU memory constraints to transform into storage capacity inquiries that IT departments are capable of managing. Still, Scality's evaluations are based on a single test bed using just two models, Gemma-3 27B and GLM-5.2. It remains to be seen whether this framework can hold up under production loads. An IT department considering this stack will require testing with its own agents, specific session volumes, and documented performance guarantees. Ultimately, the fulfillment of the promise of a fixed budget will depend on how well these requirements are met.

CIOReview
Sep 15th, 2026
CTERA Networks bridging the gap between cloud storage and local storage.

CTERA Networks bridging the gap between cloud storage and local storage. Liran Eshel, CEO, Co-Founder The explosive growth in unstructured data, the increased need to access files anywhere and from any device, and the desire to collaborate frequently across the distributed enterprise are all driving companies to use scalable, costefficient cloud storage technology to address these needs. Palo Alto based CTERA Networks caters to this need of both enterprises and service providers and in the process has created a buzz amongst the tech circles. The company enables its customers to use the cloud storage infrastructure of their choice whether private, public or hybrid, while ensuring end-to-end security and overcoming performance and scalability challenges. 'Single Pane Of Glass' Management Founded in 2008 by Liran Eshel, CEO, and Zohar Kaufman, VP R&D, the company provides a cloud storage platform that enables enterprises and service providers to create, manage and deliver cloud storage services, whether internal or external. The company's unique platform includes built-in functionality for file sync and share, hybrid on-premises/cloud backup,managed branch storage and mobile collaboration which is unified under 'single pane of glass' management. "Our platform extends storage infrastructure to make it "cloud enabled", and integrates with the majority of object-based and other scalable storage systems, as well as cloud service providers. CTERA is a technology vendor - we do not provide cloud services, but rather empower service providers and enterprises to do so," adds Eshel. It is the only company to provide a platform that addresses multiple cloud storage "killer apps" and supports both hardware and software-based deployment scenarios to optimize performance across remote office and branches,as well as mobile workers. Providing Game Changing Services CTERA's core development team originated from Check Point Software, so security is very much in the company's DNA and is embedded into all aspects of the company's solutions, which feature built-in source-based encryption and other security measures. The company's cloud storage platform is comprised of cloudside management software, cloud storage gateway appliances and ndpoint software agents for PCs and mobile. Customers can choose any mix of software/hardware to serve their storage, backup and file sharing needs in an optimal way. Active Directory integration, including support for the most complex AD forests, makes it easy for enterprises to deploy CTERA's solution, while global source-based de-duplication ensures optimal erformance over the WAN. CTERA integrates with storage infrastructure from EMC, IBM, HDS, OpenStack, Caringo, DDN, Scality and leversafe, as well as with public cloud providers such as AWS and Rackspace. Winning the Trust of Clients Due to its game changing technology, in a very short period of time, the company has won the hearts of many firms which include some of the Fortune 100 enterprises across multiple industry sectors including financial services,life sciences and energy. Major service providers also use CTERA's platform to drive their businessgrade cloud storage services.They include, among others, Telefonica, CenturyLink, Orange (France Telecom), Telecom Italia and Swisscom. With customers in production with 10,000s of appliances deployed and 100,000s of active users, CTERA's technology is proven to scale to very large deployments. CTERA plans to continue its good run and expand its platform both to support additional usecases as well as to extend further the value of cloud storage for security, compliance and data mining purposes in the future. Management Liran Eshel, CEO, Co-Founder and Zohar Kaufman, VP R&D, Co-Founder Description A provider of a cloud storage platform for enterprises and service providers, enabling the delivery of a suite of storage applications over the infrastructure of their choice

Associated Press
Sep 9th, 2026
Scality launches Maestro to manage distributed backup-as-a-service at scale

Scality has launched Maestro, a fleet management platform designed to help cloud and managed service providers operate multiple object storage deployments at scale. The platform addresses operational challenges faced by providers offering backup-as-a-service across numerous customer sites. Maestro provides centralised monitoring, automated software and firmware updates, and aggregated capacity data across all Scality ARTESCA deployments. The platform uses an out-of-band connectivity architecture that eliminates VPN requirements. Co-developed with service provider partners, Maestro is now in production use. UK-based Autodata, which manages multiple ARTESCA deployments for backup services, reports the platform enables fleet growth without additional operational staff. The solution also suits enterprises running distributed storage across branch offices or edge locations. Maestro 1.1 supports all ARTESCA deployments running version 4.0.3 or later, with a SaaS delivery model planned for future release.

VMblog
Sep 9th, 2026
Scality delivers Maestro to meet growing operational demand for backup-as-a-service.

Scality delivers Maestro to meet growing operational demand for backup-as-a-service. As cloud and managed service providers race to deliver backup-as-a-service (BaaS) at scale, a critical operational challenge has emerged: managing tens to hundreds of individual object storage deployments spread across customer sites, each requiring monitoring, maintenance, and billing reconciliation. Scality announced general availability of Scality Maestro, a fleet management platform purpose-built for service providers operating large numbers of Scality ARTESCA object storage deployments on behalf of their customers. Managing distributed object storage infrastructure at scale is fundamentally different from managing a single deployment. Service providers need a single pane of glass to monitor health, push firmware and software updates, track capacity utilization across all customer sites, and do so without requiring VPN tunnels into every customer environment. As BaaS offerings grow from dozens to hundreds of sites, this operational gap becomes a hard constraint on scalability. Co-developed with cloud and service provider partners (SCSP), Scality Maestro is now in production use across customer deployments and provides service providers with: * Centralized health monitoring and alerting across the entire Scality ARTESCA fleet * End-to-end management down to the hardware layer, including ARTESCA software upgrades and server firmware updates * Aggregated capacity utilization data across all customer deployments, enabling automated pay-as-you-go billing * Out-of-band connectivity architecture that eliminates VPN requirements, as all traffic flows from ARTESCA to Maestro, never the reverse * Support for enterprises managing ARTESCA across branch offices or distributed edge locations "We manage multiple Scality ARTESCA deployments across our key customer sites in the UK and Ireland as part of our backup-as-a-service offering, and until Maestro, every one of them was independently managed - separate logins, VPN tunnels, manual update cycles, and monthly capacity reports assembled by hand. Instead, Maestro gives us a single view of the entire fleet. Software updates that consume hours of staff time can now be monitored centrally, and we can grow the fleet without adding operational headcount. We can already foresee a significant difference in running our service." - Dominic McLouglin, CEO, Autodata While Maestro was designed primarily for the SCSP and MSP market, the platform is equally well suited to enterprise organizations running ARTESCA across multiple locations, whether at the edge, in branch offices, or as part of a distributed data protection architecture. Once deployments expand beyond a handful of sites, organizations reach an operational inflection point: tools that work well for a single site cannot efficiently manage tens or hundreds. Maestro provides the centralized management needed to operate at that scale. "Service providers building BaaS businesses on Scality ARTESCA are managing infrastructure at a scale that demands enterprise-grade operational tooling. Maestro was built with our SCSP partners to solve the real operational problems they face every day with centralized visibility, automated management, and the billing infrastructure to run a sustainable managed service. The fact that it requires no VPN and no network reconfiguration on the customer side was a hard requirement from day one." - Thomas Danan, Sr. Product Director, Scality Availability Scality Maestro 1.1 is generally available and supports all Scality ARTESCA deployments running version 4.0.3 or later. It is currently available as an on-premises deployment, with a SaaS delivery model planned for future release.v David Marshall is the founder of VMblog.com, one of the industry's longest-running independent publications covering modern data center technologies. What began as a focus on virtualization and cloud computing has expanded to cover the full spectrum of enterprise IT, including AI, security, and DevOps, making VMblog a trusted destination for vendor news, technology analysis, and industry commentary.Beyond publishing, David has spent his career at the intersection of technology and business, inventing, marketing, and launching a number of successful software companies and products, and building a reputation as a skilled marketing executive in the enterprise IT space.David is also a published author, having written two well-regarded books on virtualization and served as technical editor for two "For Dummies" titles covering virtualization and cloud computing. He co-founded CloudCow.com, a publication focused on cloud computing, and has been named a VMware vExpert every year since 2009, one of the longest continuous honoree streaks in the program's history.Connect with David on LinkedIn: https://www.linkedin.com/in/davidmarshall/

Pyramid Computer GmbH
Aug 14th, 2026
August 14, 2026 Pyramid computer and Scality launch strategic partnership for AI and object storage solutions.

August 14, 2026 Pyramid computer and Scality launch strategic partnership for AI and object storage solutions. Scality and Pyramid Computer GmbH is combining its expertise to create high-performance, reliable, and scalable data infrastructures! The partnership focuses on integrated solutions for data-intensive applications - from AI and machine learning pipelines to large-scale media workflows and enterprise-wide data management. This collaboration combines its high-density AKHET(R) server and edge computing platforms with Scality's RING and ARTESCA object storage software. This provides enterprises with a validated stack that reliably stores and manages large volumes of data and makes it available for modern AI workloads. A key benefit lies in strengthening data sovereignty. By combining its hardware infrastructure - developed in Germany - with Scality's software-defined object storage technology, Pyramid Computer GmbH create a local private cloud stack for companies that want to manage their data in a controlled, secure, and compliant manner. The solution helps customers better manage data storage locations and access paths, comply with regulatory requirements such as the GDPR, and reduce their reliance on non-European hyperscalers. This makes it a key component of long-term, resilient data strategies. These joint solutions are designed specifically for industries with very high data requirements, including broadcast and media, medical imaging, autonomous systems, and large-scale AI training environments. By combining Pyramid computing platforms with Scality's S3-compatible object storage, integration risks can be reduced and production environments can be deployed more quickly. Pyramid Computer GmbH'd like to thank Scality for the great collaboration and look forward to its future projects!