# AI Infrastructure Engineer

**Company:** [Intercom](https://hotfix.jobs/companies/intercom)
**Location:** Dublin, Ireland, London, United Kingdom
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Python, Ruby, Java, Go, CUDA, Triton, Kubernetes, AWS, Transformers, LLMs, Distributed Training, Model Inference, Gpu Optimization, Autoscaling, Data Preprocessing
**Posted:** 2026-07-20

> Build and optimize large-scale training pipelines and low-latency inference services for Fin’s AI models. The role requires strong software engineering experience, hands-on expertise in model training, inference, or GPU programming, and collaboration with ML scientists.

## Job Description

## Responsibilities
- Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation.
- Build and optimize inference services that deliver low-latency, high-reliability customer experiences, including autoscaling, routing, and fallbacks.
- Tune GPU kernels, improve utilization, and identify bottlenecks across the training and inference stack.
- Collaborate with ML scientists to implement advanced training and inference methods and bring them to production.
- Participate in hiring, mentoring, and developing engineers.
- Raise technical standards, reliability, and operational excellence across the AI platform.

## Requirements
- 5+ years of software engineering experience with a strong record of shipping high-quality products or platforms.
- Degree in Computer Science, Computer Engineering, or a related field, or equivalent experience with strong fundamentals.
- Hands-on experience with model training, particularly transformers and LLMs; model inference at scale; or low-level GPU work such as CUDA or Triton kernels.
- Experience working in production environments at meaningful traffic, data, or organizational scale.
- Clear communication and ability to explain complex technical topics to technical and non-technical audiences.
- Strong technical fundamentals and willingness to learn and develop.
- Deep knowledge of at least one programming language, such as Python, Ruby, Java, or Go.

## Nice-to-haves
- Experience at AI-native companies training or serving their own models.
- Experience running training or inference workloads on Kubernetes.
- Experience with AWS or other major cloud providers.
- Production Python experience in ML or infrastructure contexts.
- Personal projects, open-source contributions, meetups, or published technical content.

## Compensation & Benefits
- Competitive salary and equity.
- Lunch on weekdays, snacks, and a stocked kitchen.
- Regular compensation reviews.
- Unlimited access to Claude Code and other AI tools.
- Pension scheme with matching up to 4%.
- Life assurance and comprehensive health and dental insurance for employees and dependents.
- Flexible paid time off.
- Paid maternity leave and six weeks of paternity leave.
- Cycle-to-Work Scheme and secure bike storage.
- MacBooks as standard, with Windows available for certain roles.
- Hybrid working policy requiring at least three days per week in the office.

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