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Clera

Research Engineer, Mid-to-Senior

Zürich
Level
Senior
Work type
On-site · Zürich

Not stated in the posting: salary, visa.

Open. First seen 15 October 2026.

Apply on the company's site →

What this role asks for

Particular to this role

PyTorch

Rare in Clera's other 299 open postings.

Standard for software engineering roles

Python

Common in software engineering postings at any company.

Read against Clera's other open postings on this site.

The team: Engineering HIRES STEADILY

This role is one of 292 open in Engineering. 226 were posted in the last 30 days and the oldest in March: a team that hires all year rather than one being built.

WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS

LLM (135 of 292), TypeScript (131 of 292), Python (123 of 292), React (107 of 292), Machine Learning (70 of 292), PostgreSQL (58 of 292), JavaScript (55 of 292)

OTHER ROLES ON THIS TEAM

From the team and dates on Clera's own job board.

About the role

About the Role

Join an AI research team to turn experimental code into reliable, production-ready systems. You will build post-training pipelines for generating, processing, and evaluating research data, helping research ideas scale into repeatable experiments.

What You'll Do

  • Convert research prototypes into tested, reusable data generation, training, and evaluation pipelines.
  • Build distributed experiment support for data loading, checkpointing, and collecting agent interactions.
  • Profile workloads and improve GPU utilization, memory efficiency, and data throughput.
  • Develop tests, experiment tracking, and debugging tools while preserving the integrity of experimental results.
  • Collaborate with researchers to turn ideas into robust, maintainable software.

What We're Looking For

  • Typically 2 to 10 or more years of relevant experience building machine learning training, inference, or data pipelines in Python.
  • Strong Python skills and hands-on experience with PyTorch, JAX, or a comparable machine learning framework.
  • Experience building and debugging software for model training, inference, or data processing at scale.
  • Understanding of machine learning experiments, including how data, numerical precision, and implementation choices affect results.
  • Sound software engineering practices, including testing, profiling, version control, and documentation.
  • Experience with distributed tools such as PyTorch distributed, DeepSpeed, or Ray is helpful, as is familiarity with Transformers libraries, vLLM, or experiment tracking tools.
  • Background in reinforcement learning systems or working with video, audio, or image datasets is a plus.

Compensation & Benefits

Competitive compensation with equity is offered, along with health, dental, and vision coverage and professional development support. Visa sponsorship and relocation support are available.

Location

This is a full-time, on-site role in Zürich, Switzerland.

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