Junior Machine Learning Engineer

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.

• 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.