Multiverse Computing
Multiverse is a well-funded, fast-growing deep-tech company founded in 2019. We are the largest quantum software company in the EU and have been recognized by CB Insights (2023 and 2025) as one of the 100 most promising AI companies in the world.
With 180+ employees and growing, our team is fully multicultural and international. We deliver hyper-efficient software for companies seeking a competitive edge through quantum computing and artificial intelligence.
Our flagship products, CompactifAI and Singularity, address critical needs across various industries:
· CompactifAI is a groundbreaking compression tool for foundational AI models based on Tensor Networks. It enables the compression of large AI systems—such as language models—to make them significantly more efficient and portable.
· Singularity is a quantum- and quantum-inspired optimization platform used by blue-chip companies to solve complex problems in finance, energy, manufacturing, and beyond. It integrates seamlessly with existing systems and delivers immediate performance gains on classical and quantum hardware.
You’ll be working alongside world-leading experts to develop solutions that tackle real-world challenges. We’re looking for passionate individuals eager to grow in an ethics-driven environment that values sustainability and diversity.
We’re committed to building a truly inclusive culture—come and join us.
As a Machine Learning Engineer, you will
- In general: 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 reinforcement learning methods to enhance model capabilities and safety.
- Optimize and specialize models: apply compression techniques to meet performance and footprint targets.
- Collaborate across research and engineering and contribute to our shared codebase; uphold engineering standards.
Required Qualifications
- This role requires a Bachelor’s degree in ML, Computer Science, Math, Physics, or related field.
- Upto 2 years of hands-on experience. Strong programming skills in Python and the modern ML stack and basic software practices (git, unit tests, CI).
- Solid grounding in language modelling concepts around training, evaluation, model architecture, and data.
- 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.
- Familiarity with AI / agentic workflows, and task automation.
Preferred Qualifications
- PhD in ML, Computer Science, Math, Physics, Data Science, 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 task‑grounded evals for LLMs, implementing or extending evaluation harnesses; deep understanding of LLM capabilities and 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 RL theory, frameworks, and monitoring.
Perks & Benefits
- Indefinite contract.
- Equal pay guaranteed.
- Variable performance bonus.
- Signing bonus.
- We offer work visa sponsorship (If applicable).
- Relocation package (if applicable).
- Private health insurance.
- Flexible remuneration: hospitality and public transportation.
- Eligibility for educational budget according to internal policy.
- Hybrid opportunity.
- Flexible working hours.
- Language classes and discounted lunch options
- Working in a high paced environment, working on cutting edge technologies.
- Career plan. Opportunity to learn and teach.
- Progressive Company. Happy people culture
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.
QUANTUM ROLE CONTEXT | Appended by Quantum.Jobs v2
Role context:
This early-career engineering function exists to design, implement, and maintain end-to-end artificial intelligence models and data pipelines. Positioned within core technical teams, the role connects raw algorithm research with production software deployment. Engineers in this position focus on building training routines, setting up validation workflows, and optimizing model efficiency. By translating initial research concepts into reliable code, they ensure that underlying computational architectures perform effectively within broader enterprise software systems and customer-facing software applications.
Quantum ecosystem relevance:
The position supports the quantum ecosystem by helping integrate classical machine learning methods with quantum-inspired tensor networks and optimization algorithms. As deep-tech organizations bridge high-performance computing and quantum software, software engineers are required to implement and test specialized compression and evaluation routines. By ensuring efficient model execution across distributed computing environments, this technical function helps refine hybrid algorithms and supports the practical commercialization of advanced quantum-inspired optimization software for industrial application areas.
Capability signals:
- Hands-on proficiency in building and validating machine learning data and training pipelines
- Technical knowledge of distributed computing resources and containerized deployment software workflows
- Expertise in applying model compression techniques including pruning, quantization, and tensor networks
- Ability to design evaluation frameworks for testing model alignment, competence, and reliability
- Capacity to prototype research papers quickly into production-ready Python codebase environments
