Research Engineer, Mid-to-Senior

Clera

Zürich, Switzerland

On-siteCareer-pivot friendly

Who this role is best for

Aimed at mid-to-senior software engineers with machine learning pipeline experience who can work collaboratively in an on-site setup in Zürich.

Best fit for

Candidates with 2+ years of experience in building ML pipelines and a track record of debugging at scale
— “2 to 10 or more years of relevant experience building machine learning training, inference, or data pipelines in Python.”
Individuals who have worked on distributed ML systems and can optimize GPU and data performance
— “Profile workloads and improve GPU utilization, memory efficiency, and data throughput.”
Candidates with a background in reinforcement learning or experience handling video, audio, or image datasets
— “Background in reinforcement learning systems or working with video, audio, or image datasets is a plus.”

Things to consider

On-site presence in Zürich is required, which may affect relocation plans or availability
— “This is a full-time, on-site role in Zürich, Switzerland.”
The role may demand a high level of collaboration with researchers, which requires adaptability
— “Collaborate with researchers to turn ideas into robust, maintainable software.”

How to stand out

Highlight your experience with experiment tracking and debugging in your resume and interview
— “Develop tests, experiment tracking, and debugging tools while preserving the integrity of experimental results.”
Showcase your ability to build reusable and scalable data or training pipelines
— “Convert research prototypes into tested, reusable data generation, training, and evaluation pipelines.”
Emphasize your hands-on ML framework experience with PyTorch, JAX, or similar
— “Strong Python skills and hands-on experience with PyTorch, JAX, or a comparable machine learning framework.”
Demonstrate your understanding of how implementation choices impact ML results
— “Understanding of machine learning experiments, including how data, numerical precision, and implementation choices affect results.”
Show familiarity with distributed tools like PyTorch distributed or Ray
— “Experience with distributed tools such as PyTorch distributed, DeepSpeed, or Ray is helpful.”

Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

building production-ready ML systems
improving GPU utilization
developing robust software

Typical background

software engineeringmachine learningdata science

Skills & requirements

Required

Machine LearningData PipelinesSoftware EngineeringExperiment Tracking

Preferred

Reinforcement LearningVideo/audio/image Datasets

Stack & domain

PythonPyTorchJAXMachine LearningData PipelinesProblem-solvingTeamworkAIData Processing

About the role

Original posting from Clera via Ashby

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.

Source: Clera careers (Ashby)