The ML Engineer will build, deploy, and maintain production-grade machine learning services and tooling in the advertising domain, focusing on MLOps and SRE principles.
Responsibilities
building, deploying and maintaining production-grade machine learning services and specialised tooling
designing and orchestrating ML engineering workflows, including model packaging, deployment strategies, observability and operational stability
productionising machine learning models in close collaboration with Data Science and Research teams
developing and maintaining automated deployment pipelines, model monitoring, alerting and rollback strategies
ensuring reproducible model packaging and reliable model lifecycle management
Lead the security engineering capability for Sand Technologies, focusing on cloud security across AWS, Azure, and GCP, and managing incident response, vulnerability pipelines, and compliance readiness.
Responsibilities
Serve as second responder on security incidents across Sand
Operate and tune our security stack: Microsoft Defender for Cloud/Endpoint, Intune, Sentinel, and vulnerability scanning across a multi-account AWS organization, GCP, and Azure
Build and maintain the vulnerability data pipeline: raw exports from cloud-native tools, deduplication by unique CVE, finding-age and SLA computation, and the feeds behind our security metrics
Run tooling validations — side-by-side parity testing when we transition platforms, with evidence that stands up to audit
Partner with infrastructure/DevOps teams on remediation: container base-image hygiene, registry lifecycle policies, exposure management, and verifying closure on the next scan
Design and scale data pipelines and machine learning infrastructure for an AI-first platform, using AI tools to multiply output and working with product/ML engineers.
Compensation
The base salary is $170,000 to $200,000 for US-based candidates, plus equity.
Responsibilities
Design and build reliable data pipelines that process large volumes of customer warehouse data
Build and run the machine learning operations infrastructure that takes models from training to production: feature pipelines, training jobs, deployment, and monitoring
Own data quality and data modeling to ensure accurate and trustworthy data
Use AI coding tools effectively by writing clear specs and context, running agents on parallel workstreams, and reviewing their output critically
Build shared AI workflows, guardrails, and review standards
We are looking for a Senior QA Engineer to join our Discovery Platform team to engineer quality into complex backend systems and ensure reliability and safe releases.
Locations (2)
Prague, Prague, Czech Republic
EU
Compensation
25 days of holidays + 5 flexi-days. Pluxee Flexi Card (up to 16 000 CZK per year - valid only for Czech employees). Anniversary rewards.
Responsibilities
Closely collaborate with developers to deliver features & fixes in quality
Bring quality mindset to the whole development cycle from planning to release
Improve the way we work
Define and monitor quality metrics
Write automated tests and participate in manual testing
Requirements
Nice to have: AI-assisted development workflows
Nice to have: Playwright
Nice to have: Python
Nice to have: Networking
Nice to have: Network Engineering
Home office support (IT equipment and €540 furniture budget)
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