The posting
Deeplab combines high-end software technologies with state-of-the-art machine learning and deep learning research to deliver services and products that tackle challenging real-life problems. This is your chance to join a dynamic and rapidly evolving ML/AI organization committed to research, innovation, andexcellence. We foster a culture that bridges academic rigor with entrepreneurial agility — an environment where methodical research meets real-world impact and fast-paced innovation. This synergy fuels our mission to solve complex, meaningful problems using cutting-edge machine learning.
The Team
We are gradually expanding our R&D teams, with a growing pipeline of challenging new projects that need strong technical owners. We handle disruptive, high-risk/high-impact work end-to-end — from ideation through proposal compilation, proof-of-concept development, and final delivery. Our portfolio spans adTech systems serving millions of daily users, drug discovery with virtual screening of billions of molecules, cancer immunotherapies, brain-computer interfaces, and more. You will join an interdisciplinary team of passionate ML engineers with backgrounds in both research and software development.
The Role
We are looking for an accomplished and driven ML Engineer with a strong engineering foundation, proven research output, and substantial hands-on development experience. The ideal candidate brings a broad and deep skill set rooted in core machine learning, with demonstrated application experience in areas such as computer vision, natural language processing, signal processing, LLM-based/generative AI systems, or related fields. Your primary focus will be contributing to — and in some workstreams co-leading — EU-funded R&D projects (e.g., Horizon Europe), taking ownership of technical deliverables from experimental design through implementation and reporting. You will also dedicate a portion of your time to the technical preparation and creative writing of new research and innovation proposals. Alongside project delivery, you will develop proof-of-concept systems that translate state-of-the-art research into working prototypes, and you will help elevate the team’s practices through knowledge-sharing and light mentoring of junior colleagues. This is a role for someone who thrives at the intersection of rigorous ML research and engineering execution, and who is ready to take on greater autonomy and responsibility within a collaborative, international R&D setting.
Responsibilities
- Design, develop, and implement machine learning and deep learning algorithms as part of funded EU R&D projects, taking ownership of technical workstreams and deliverables.
- Conduct in-depth literature surveys, experimentation, and benchmarking to advance project objectives across diverse research topics.
- Develop proof-of-concept systems that incorporate state-of-the-art methods and translate them into demonstrable prototypes.
- Prototype and evaluate LLM-based and agentic system components — e.g., retrieval-augmented generation pipelines, tool-using agents, and evaluation harnesses — selecting appropriate modern frameworks and tooling as projects require.
- Perform statistical modeling, machine learning model development and evaluation, data analytics, and dataset management to ensure data quality and experimental rigor.
- Optimize models and pipelines for scalability, efficiency, and reproducibility.
- Contribute to the planning, execution, and timely delivery of project milestones and technical reports.
- Participate in the preparation and creative writing of technical content — including state-of-the-art reviews — for EU grant proposals and industrial R&D calls.
- Collaborate with ML engineers, domain experts, and software developers in cross-functional, international teams.
- Share knowledge and help establish best practices within the team; provide guidance to more junior engineers where appropriate.
- Stay current with ML research and developments relevant to Deeplab’s active and upcoming projects.
Requirements
- MSc in Electrical & Computer Engineering, Computer Science, Machine Learning, Applied Mathematics, Statistics, Physics, Signal Processing, or a closely related quantitative field. A PhD is a plus but not required.
- 4+ years of hands-on ML experience, with clear evidence of having owned technical workstreams end-to-end — scoping problems, choosing approaches, delivering results, and documenting them. Raw years are not the signal on their own; we want to see that those years translated into real ownership and autonomy.
- Strong, broad foundations in core ML — including deep learning architectures, optimization, probabilistic modeling, and the mathematical and statistical principles that underpin them.
- Demonstrated application experience in at least one major ML domain (e.g., computer vision, NLP, signal/audio processing, time-series analysis, or LLM-based/generative AI systems), with openness and ability to work across domains.
- Proficient in Python, with working fluency in at least one major deep learning framework (PyTorch, JAX, or TensorFlow).
- Mature engineering practices in ML work. This is a research-heavy role, but we expect the engineering to be solid — not just throwaway notebook code. We’ll look for signals such as experiment tracking (MLflow, W&B, or equivalent), version-controlled and tested codebases, reproducible pipelines, or CI workflows. Model deployment experience is a plus; what matters most is that you treat ML code as software, whether it ships to users or supports a research deliverable.
- Experience with technical and scientific writing — proposals, technical reports, or publications.
- Comfortable with Linux environments, version control (Git), CI/CD workflows, containerization (Docker), and collaborative development practices.
- Strong problem-solving ability: can decompose complex, ambiguous problems and formulate viable ML solutions independently.
- Excellent written and oral communication skills in English; ability to present complex analyses clearly and work effectively in international teams.
Strong Pluses
- Exposure to or active interest in computational biology, bioinformatics, or life-sciences applications of ML (e.g., molecular property prediction, omics data analysis, drug discovery pipelines).
- Hands-on experience building and evaluating LLM-based or agentic systems — e.g., retrieval-augmented pipelines with measured retrieval quality, systematic evaluation harnesses, model adaptation (LoRA/DPO-style fine-tuning), tool-using agents with robust error handling, or LLM inference optimization. Depth matters more than framework familiarity: we care that you can measure, debug, and improve these systems, not which library you used.
- Experience contributing to or delivering funded collaborative research projects (e.g., Horizon Europe or similar programmes).
- Experience with large-scale cluster computing, distributed training, or HPC for ML workloads.
- Publications or patents. Peer-reviewed papers, patents, or substantive technical reports. Quality over quantity; first-author publications at top-tier ML venues (such as NeurIPS, ICML, ICLR, CVPR, ACL, or EMNLP) or their workshops are a strong signal, but good work at other reputable venues or in applied domains also counts.
Benefits
- Supplementary private health insurance.
- Flexible working hours and remote work opportunities.
- Work on advanced AI with real-world impact.
- Budget for home office equipment and productivity.
- Personal development budget and knowledge-sharing sessions.
- Newly designed and inspiring office environment.
- Competitive salary based on experience and qualifications.
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