Machine Learning Engineer, Multiverse Computing
Product, Fixed-Term Contract
€45-51.8k
More information about location
3+ days a week in office
Quantum software for complex challenges
Open for applications
Quantum software for complex challenges
101-200 employees
Open for applications
€45-51.8k
More information about location
3+ days a week in office
101-200 employees
Company mission
To bring quantum computing to society and use cases with great impact on society and on the technological wealth of the national and international territory.
Role
Who you are
- This role requires a Bachelor's 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, production‑ready code
- Clear, pragmatic communication and a collaborative mindset
Desirable
- 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
- Model Evaluation: track record building task‑grounded 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: Hands‑on experience debugging multi‑node 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 trade‑offs and to integrate results into production
- Post‑Training: Theoretical and practical familiarity with post-training and alignment techniques; experience with SFT and preference/RL‑based methods (e.g., DPO/GRPO, RLHF)
What the job involves
- Fixed-term contract ending in June 2026
- Build data and model pipelines end‑to‑end: create, source, augment, and validate datasets; stand up training/fine‑tuning/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 systems‑level speedups
- Improve models post‑training: apply SFT and preference‑based 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, well‑tested Python; document decisions and artifacts; uphold engineering standards
Application process
- We are looking to fill this role immediately and are reviewing applications daily. Expect a fast, transparent process with quick feedback
Our take
Multiverse Computing is a company working on quantum and quantum-inspired computing solutions, addressing the limitations of traditional computing across finance, energy, manufacturing, and healthcare.
With its flagship products Singularity and CompactifAI, the company democratises quantum computing access, enabling professionals to leverage advanced algorithms for AI-driven optimisation and data compression without prior quantum expertise.
The company's had a successful 2025 too, first being named on the 2025 CB Insights' List of the 100 Most Innovative AI startups and then announcing a whopping $215M funding round. With this additional capital, the company is focused on strengthening CompactifAI, and further accelerating its name on a global scale.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 10 rounds)
Jun 2025
$219.7m
SERIES B
Mar 2025
$75.6m
GRANT
Total funding: $354.4m
Company HQ
Miramón-Zorroaga, Donostia-San Sebastian, Spain
Leadership
Enrique Lizaso Olmos
(Founder & CEO)Founder of BlockTac and President of the 'Quantum for Quants' Commission at the Quantum World Association. Previously taught Statistical Methods for Engineering at the University of Vic - Central University of Catalonia.
Samuel Mugel
(CTO)Mentor at the Creative Destruction Lab, and Scientific Advisor for the McKinsey Technology Council and Quantum World Association. Previously a Computational Physicist at Cortirio and a Technical Director at Quantum Ventures.
Partner and CEO at Entanglement Partners, as well as the General Manager at The Carrot Cake. They are also on multiple Advisory Boards, including greenYng, Ubiquat Technologies, and Ontek. Previously co-founded Ontek, Hubtype, and Cabier Institute.
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