Senior ML Researcher

Job Description

General Info

We are developing a breakthrough imaging and 3D technology platform currently under provisional patent. Our Budapest engineering team works at the edge of what mobile hardware and modern AI make possible, in a well-funded, non-corporate environment built around craftsmanship and technical excellence.

As Senior Machine Learning Researcher, you will be a deeply technical individual contributor focused on the research side of the model stack — formulating and investigating novel methods in computer vision, 3D reconstruction, neural rendering, and related imaging ML domains, and translating promising directions into prototypes that the engineering team can productionise. You will work closely with the Lead Machine Learning Engineer on overall ML strategy, partner with ML engineers on the research-to-production handoff, and contribute directly to the Company’s patentable methods. This role is for a research-minded scientist who can take open problems, formulate them rigorously, run principled experiments, and produce both working code and clear written conclusions.

Key Responsibilities

Research & Method Development

• Formulate, investigate, and prototype novel ML and deep-learning methods for the Company’s core imaging and 3D problems — including computer vision, 3D reconstruction, neural rendering, and related domains.
• Continuously track the state of the art in relevant fields; identify promising research directions, replicate key results, and assess applicability to the Company’s problems.
• Design and run principled experiments — ablations, controlled comparisons, sensitivity analyses — with the rigour expected of peer-reviewed work.
• Produce clear written research artifacts: technical notes, internal papers, and reproducible experiment reports that the rest of the team can build on.
• Contribute substantively to the Company’s patentable methods and to IP-related discussions where novel approaches may warrant patent filings.

Experimentation & Evaluation

• Drive the experimental methodology for owned research areas: hypothesis formulation, training recipes, hyperparameter studies, and statistical treatment of results.
• Contribute to the Company’s evaluation methodology — metric design, test sets, held-out benchmarks, failure-mode analysis, and uncertainty quantification — with a particular eye to scientific defensibility.
• Critically assess what models do and do not do; surface failure modes early and characterize them with the same rigour as positive results.
• Apply experiment tracking, reproducibility, and versioning practices rigorously, so that every reported result can be reproduced from logged artifacts.

Research-to-Production Handoff

• Convert research prototypes into clean reference implementations that the engineering team can productionise — not throwaway notebooks.
• Partner with ML engineers on the path from research code to training-cluster-ready and ultimately production-ready models.
• Provide ongoing scientific input to productionised models: re-evaluation under shifted conditions, new data, and adversarial cases.

Cross-Functional Collaboration

• Partner with the Lead Machine Learning Engineer on the overall ML roadmap, advocating for research bets that warrant investment.
• Partner with the imaging and product teams to translate model capabilities and limitations into concrete product implications.
• Represent the research perspective in strategic and IP-related conversations across the Company.
• Mentor more junior engineers and researchers on owned research areas as the team grows.

Qualifications

Required

• PhD in Computer Science, Machine Learning, Computer Vision, Electrical Engineering, Imaging Science, Applied Mathematics, Physics, or a closely related research field.
• Five or more years of post-PhD research experience, or equivalent depth through industry research roles, in deep learning applied to vision, 3D, or related imaging domains. Substantial industry research experience predating or overlapping the PhD may count toward this total.
• Demonstrated track record of original research output — peer-reviewed publications at major venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, SIGGRAPH, or similar), substantive open-source contributions, or comparable externally visible work.
• Strong proficiency in Python and modern ML frameworks (PyTorch preferred; JAX or similar welcomed) at a level that supports first-author research code, not only consumer-of-libraries usage.
• Solid working knowledge of training models on GPU clusters, including distributed training and mixed-precision — enough to run the experiments your research requires without blocking on engineering.
• Strong research instincts: ability to read, critique, and replicate papers; to distinguish signal from hype; to formulate hypotheses precisely; and to design experiments that meaningfully test them.
• Scientific honesty about what models actually do and do not do; willingness to publish negative or null results internally with the same rigour as positive ones.
• Ability to handle confidential and pre-patent technical material with discretion, and to follow the Company’s IP and data-handling policies rigorously.
• Fluency in English (written and spoken); able to work full-time in Budapest, Hungary.

Strongly Preferred

• Direct research experience in computer vision, 3D reconstruction, neural rendering (NeRF, Gaussian splatting, or related), or generative imaging models.
• Prior experience in an industry research lab, frontier ML lab, or research-led startup — with successful translation of research output into shipped product or filed patents.
• Working knowledge of inference optimisation (quantisation, pruning, distillation) sufficient to design research with downstream deployment in mind.
• Experience with sensor-based modelling, hyperspectral / multispectral imaging, or other physically grounded imaging problems.
• Prior experience mentoring junior researchers or co-supervising research students.

Compensation

In accordance with Hungary’s implementation of the EU Pay Transparency Directive (Directive (EU) 2023/970):

• Salary range: HUF 2,000,000 – HUF 2,500,000 gross per month, depending on research track record and demonstrated technical depth.
• Discretionary performance bonus, with details discussed with shortlisted candidates.
• Statutory and customary Hungarian benefits.

Compensation decisions are made on the basis of objective, gender-neutral criteria, including relevant experience, technical and research depth, scope of contribution, and demonstrated performance. The Company does not request salary history during the recruitment process.