6 дней назад
Principal Generalist Engineer (Machine Learning)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Principal Generalist Engineer (Distributed Systems and ML): Building reliable, scalable, and observable production systems across application services, infrastructure, data pipelines, and machine learning with an accent on end-to-end ownership, system reliability, and performance. Focus on designing systems from ambiguous problems, debugging failures across multiple layers, deploying reproducible ML pipelines, and optimizing latency, cost, and operational performance.
Location: Remote, Spain
Company
A financial technology platform providing payments, banking, credit, cross-border, and business management tools for businesses and individuals.
What you will do
- Design and build reliable, scalable, observable production-grade systems.
- Own the full lifecycle from ambiguous problem definition and system design through implementation, deployment, and production operations.
- Work across application services, distributed systems, infrastructure, data pipelines, and machine learning systems.
- Debug complex production issues across multiple technical layers and improve performance, latency, reliability, and cost efficiency.
- Contribute to architecture, technical direction, maintainable code, documentation, and engineering standards.
- Frame ML problems appropriately, train and evaluate models, build reproducible pipelines, deploy models, and monitor performance, drift, and cost.
Requirements
- Strong computer science fundamentals, including data structures and algorithms, operating systems, networking, and distributed systems.
- Solid probability and statistics knowledge, with experience building production systems at scale.
- Ability to work across Go, Java, Python, Rust, and SQL.
- Understanding of system behavior under load and failure, with strong debugging and first-principles reasoning skills.
- Comfort with Linux, containers, Kubernetes, data systems, streaming systems, ML infrastructure, and performance optimization.
- Ability to reason about invariants, failure modes, technical trade-offs, and system reliability.
Culture & Benefits
- People-first environment focused on well-being, inclusion, and respect.
- Culture of ownership, simplicity, curiosity, robust design thinking, and clear communication.
- Learning and development support through knowledge sharing, training, and internal technical talks.
- Salary, pension, health insurance, paid leave, and additional benefits.
Hiring process
- Initial conversation with a recruiter.
- Take-home technical assessment followed by an interviewer discussion.
- System design interview covering architecture and problem-solving, followed by a final technical and behavioral interview with an executive team member.
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