Machine Learning Engineer (Product) (9-month contract with bonus incentives)
Machine Learning Engineer (Product) (9-month contract with bonus incentives)
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Job Description
We are looking to fill this role immediately and are reviewing applications daily. Expect a fast transparent process with quick feedback.
Why join us
We are a European deep-tech leader in quantum and AI backed by major global strategic investors and strong EU support. Our groundbreaking technology is already transforming how AI is deployed worldwide compressing large language models by up to 95% without losing accuracy and cutting inference costs by 5080%.
Joining us means working on cutting-edge solutions that make AI faster greener and more accessible and being part of a company often described as a quantum-AI unicorn in the making.
We offer
- Competitive annual salary starting from 45000 based on experience and qualifications.
- Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion.
- Relocation package (if applicable).
- Up to 9-month contract ending on June 2026.
- Hybrid role and flexible working hours.
- Be part of a fast-scaling Series B company at the forefront of deep tech.
- Equal pay guaranteed.
- International exposure in a multicultural cutting-edge environment.
As a Machine Learning Engineer you will
- Build data and model pipelines endtoend: create source augment and validate datasets; stand up training/finetuning/evaluation flows; and ship models that meet product and customer requirements.
- Design rigorous evaluation frameworks to verify task competence and alignment; implement statistical testing reliability checks and continuous evaluation.
- Scale training and inference: make effective use of distributed compute optimize throughput/latency and identify opportunities for algorithmic or systemslevel speedups.
- Improve models posttraining: apply SFT and preferencebased or reinforcement learning methods to enhance helpfulness safety and reasoning.
- Optimize and specialize models: apply compression techniques to meet performance and footprint targets.
- Collaborate across research and engineering: partner with ML engineers researchers and software engineers on data curation evaluation design training runs model serving and observability.
- Contribute to our shared codebase: write clean welltested Python; document decisions and artifacts; uphold engineering standards.
Required Qualifications
- This role requires a Bachelors degree in Computer Science Math Physics Physics Data Science Operations Research or related field.
- Strong programming skills in Python and the modern ML stack (e.g. PyTorch) plus fluency with data tooling (NumPy/Pandas) and basic software practices (git unit tests CI).
- Solid grounding in language modelling concepts around training evaluation model architecture and data.
- Comfort working with datasets at scale: collection cleaning filtering labelling/annotation strategies and quality controls.
- Experience using GPU resources and familiarity with containerized workflows (e.g. Docker) and job schedulers or cloud orchestration.
- Ability to read research papers prototype ideas quickly and turn them into reproducible productionready code.
- Clear pragmatic communication and a collaborative mindset.
Preferred Qualifications
- PhD in Computer Science Math Physics Data Science Operations Research or related field or equivalent industry experience in machine learning data science or related roles with demonstrated experience with NLP or LLMs.
- Experience building foundational LLMs from the ground up
Preferred qualifications by focus area:
- Model Evaluation: track record building taskgrounded evals for LLMs implementing or extending evaluation harnesses and generating synthetic data for both evaluation and training; deep understanding of LLM quirks and their ties to architecture and training dynamics.
- Distributed Training: Handson experience debugging multinode training profiling/optimizing throughput and memory and extending training frameworks like to new architectures or optimizers; comfort diagnosing flaky cluster issues.
- Model Compression: Strong mathematical background and experience with pruning quantization and NAS; ability to formulate and solve constrained optimization problems for accuracy/latency/footprint tradeoffs and to integrate results into production.
- PostTraining: Theoretical and practical familiarity with post-training and alignment techniques; experience with SFT and preference/RLbased methods (e.g. DPO/GRPO RLHF).
About Multiverse Computing
Founded in 2019 we are a well-funded fast-growing deep-tech company with a team of 180 employees worldwide. Recognized by CB Insights (2023 & 2025) as one of the Top 100 most promising AI companies globally we are also the largest quantum software company in the EU.
Our flagship products address critical industry needs:
- CompactifAI a groundbreaking compression tool for foundational AI models reducing their size by up to 95% while maintaining accuracy enabling portability across devices from cloud to mobile and beyond.
- Singularity a quantum and quantum-inspired optimization platform used by blue-chip companies in finance energy and manufacturing to solve complex challenges with immediate performance gains.
Youll be working alongside world-leading experts in quantum computing and AI developing solutions that deliver real-world impact for global clients. We are committed to an inclusive ethics-driven culture that values sustainability diversity and collaboration a place where passionate people can grow and thrive. Come and join us!
As an equal opportunity employer Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all different backgrounds including age citizenship ethnic and racial origins gender identities individuals with disabilities marital status religions and ideologies and sexual orientations to apply.
Employment Type
Full Time
