# Staff AI Research Engineer - ML Engineering

**Company**: Helsing
**Location**: Berlin
**Experience**: staff
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/helsing/jobs/4778869101?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_893d7ea4-046

## Description

Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards.

As a member of a cross-functional team at Helsing, you will architect and implement the tools and platforms that enable breakthroughs in autonomous decision-making for defence. Your focus will be on abstracting complex distributed systems to maximise training throughput and developer velocity.

## Responsibilities

- Extend our highly integrated deep learning frameworks (built on top of PyTorch), making them efficient and easy to use for a wide range of use cases.

- Scale our current infrastructure and tooling stack to support faster and larger distributed training.

- Design data strategy to support large scale datasets and efficient storage, ensuring GPUs stay warm.

## Requirements

- Hold an MSc or PhD in Computer Science or STEM field, with a focus on Machine Learning and Deep Learning.

- Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX/TensorFlow).

- Be a clear communicator who can build from complex theoretical concepts and contribute to the company's internal engineering culture.

- Have a "first-principles" mindset: you enjoy reading the latest AI optimisation blog posts and integrating them into codebases rapidly.

- Have debugged production ML pipelines and can tell a good war story about finding a subtle numerical or performance issue.

## Nice to Have

- Hands-on experience training models on large-scale GPU clusters, implementing advanced parallelism strategies, and understanding the underlying cross-node communication patterns (NCCL, MPI).

- Experience working with large-scale datasets made of different modalities.

- Proficiency with workload orchestrators like Slurm, Kubernetes, or Ray, having used them to schedule and manage complex, concurrent training jobs at scale.

- Understanding of GPU architecture at a low level: memory hierarchies, warp execution, and what makes a GPU suited for training versus inference workloads.

## Benefits

- Competitive salary and VSOP options

- Relocation support: up to €2,500 and 4 weeks temporary accommodation

- Learning: €500/£450 yearly allowance

- Health & wellness: gym membership and mental health support (Nilo.health)

- Social: regular company events and monthly social allowances

- Enhanced parental leave: 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers

- Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work

## Skills

### Required
- Python
- PyTorch
- JAX
- TensorFlow
- Machine Learning
- Deep Learning

### Nice to have
- GPU clusters
- parallelism strategies
- cross-node communication patterns
- large-scale datasets
- workload orchestrators
- GPU architecture

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/helsing/jobs/4778869101?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)