обновлено 9 дней назад
AI Infrastructure Engineer (AI)
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
AI Infrastructure Engineer (AI): Building and scaling training pipelines and low-latency inference services for Fin's transformer and LLM-based customer agent, with an accent on distributed training, GPU performance, and production reliability. Focus on tuning CUDA and Triton kernels, optimizing autoscaling and routing, and bringing advanced training and inference methods into production.
Location: Dublin, Ireland; hybrid work with at least three days per week in the office
Company
Fin, part of Salesforce, develops AI customer agents and integrated customer support products for businesses.
What you will do
- Implement and scale training pipelines for large transformer and LLM models, from data ingestion through distributed training and evaluation.
- Build and optimize low-latency, highly reliable inference services with autoscaling, routing, and fallback mechanisms.
- Tune GPU kernels, improve utilization, and identify performance bottlenecks across the training and inference stack.
- Collaborate with ML scientists to productionize advanced training and inference methods.
- Hire, mentor, and develop engineers while raising technical, reliability, and operational standards.
Requirements
- Senior-level experience with 5+ years in software engineering and a strong record of delivering high-quality products or platforms.
- Hands-on experience with model training, model inference at scale, or low-level GPU development using technologies such as CUDA or Triton.
- Experience working in production environments at meaningful traffic, data, or organizational scale.
- Deep knowledge of at least one programming language, such as Python, Ruby, Java, or Go.
- Strong technical fundamentals, clear communication skills, and the ability to collaborate with technical and non-technical stakeholders.
- Degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
Nice to have
- Experience at an AI-native company training or serving its own models.
- Experience running training or inference workloads on Kubernetes.
- Experience with AWS or other major cloud providers.
- Production experience with Python in ML or infrastructure contexts.
- Open-source contributions, personal technology projects, meetups, or technical publishing.
Culture & Benefits
- Hybrid work policy combining in-person collaboration with flexibility to work from home.
- Benefits and resources supporting health, wellbeing, family needs, time away, and financial security.
- Open and accepting workplace focused on collaboration and product impact.
- Equal opportunity employment and inclusive hiring practices.
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