ML Engineer, Model Compression
At a glance
Mid-level Machine Learning Engineering role at Qutwo. Helsinki · full-time.
Pay not stated ML Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Turn research and client models into production systems that meet real memory, latency and hardware limits
- Profile memory, bandwidth and compute use, then choose optimizations based on where models spend resources
- Compress models with quantisation, pruning and distillation, then restore accuracy through fine-tuning or other methods
- Adapt models for particular domains with retrieval, tool integrations, structured prompts or agent loops
- Take optimized models through deployment in client environments and QuTwo OS
What you bring
- Hands-on experience applying quantisation, pruning and distillation to real models, including work to recover accuracy
- Several years shipping models into production in industry or applied research
- Experience optimizing models for particular accelerators, plus a working grasp of their trade-offs
- Fluency with open-weight model families and related tooling, and Python plus C/C++ skills
Who this fits
This role suits a curious, hands-on engineer who enjoys working below the framework level and moving between projects and hardware stacks. You’ll work closely with research, engineering and client teams, taking ownership from initial idea through deployment.
From the employer
Join QuTwo, a European AI lab for the quantum era, on our quest to bridge classical AI and quantum computing systems.
A trained model is only half the story. The other half is making it run fast, lean and reliably on the hardware where it actually has to live. As an ML Engineer you will take models from our research team and clients and turn them into efficient, production-ready systems.
You will join a small team that cares about every millisecond and megabyte. If you are a humble learner, curious about what sits beneath the framework, we encourage you to apply.
Role Description
You will sit where research becomes something people can use. You take trained models from our researchers and clients and make them run within real memory, latency and hardware limits, all the way to deployment in client environments and in QuTwo OS, the intelligence layer for industrial AI.
You will work closely with our researchers, engineers, and client teams in a fast-moving environment, taking initiative to bring research into production.
Qualifications
Model compression in practice. You have applied quantisation, pruning and knowledge distillation to real models, and know how to recover accuracy afterwards through fine-tuning, retraining or other healing methods.
Profiling, not just benchmarking. You can find where a model spends its memory, bandwidth and compute, and turn that into concrete optimisation decisions.
Hardware-aware optimisation. You have optimised models for specific accelerators such as GPUs, NPUs or TPUs, and understand the trade-offs that come with each.
Open-weight models. You are fluent with today's open-weight model families and the tooling around them.
Domain adaptation harnesses. You have built the scaffolding that makes a general model useful in a specific domain, such as retrieval pipelines, tool integrations, structured prompting or agent loops, and know when that beats fine-tuning.
Python and C/C++. Python to work with research code and model artefacts; C/C++ for low-level optimisation where Python overhead is not an option.
Industry experience. Several years of hands-on work shipping models to production, in industry or in an applied research lab
Comfort with change. You enjoy switching between projects and hardware stacks, and you take ownership from first idea to final deployment.
Experience in one or more of the following areas is a plus but not a requirement:
Custom kernel development with CUDA, Triton, HIP/ROCm or Metal
On-device and edge ML, for example on NVIDIA Jetson, mobile SoCs or embedded hardware
Optimising concurrency, parallel execution and memory bandwidth for efficient inference
Model serving and deployment at scale
Generating synthetic data to fine-tune or heal compressed models
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