R D Machine Learning Engineer

Role overview

Responsibilities

• check_circle 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.
• check_circle Conduct in-depth literature surveys, experimentation, and benchmarking to advance project objectives across diverse research topics.
• check_circle Develop proof-of-concept systems that incorporate state-of-the-art methods and translate them into demonstrable prototypes.
• check_circle 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.
• check_circle Perform statistical modeling, machine learning model development and evaluation, data analytics, and dataset management to ensure data quality and experimental rigor.
• check_circle Optimize models and pipelines for scalability, efficiency, and reproducibility.
• check_circle Contribute to the planning, execution, and timely delivery of project milestones and technical reports.
• check_circle 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.
• check_circle Collaborate with ML engineers, domain experts, and software developers in cross-functional, international teams.
• check_circle Share knowledge and help establish best practices within the team; provide guidance to more junior engineers where appropriate.
• check_circle Stay current with ML research and developments relevant to Deeplab’s active and upcoming projects.

Basic qualifications

• 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.
• 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

• check_circle Supplementary private health insurance.
• check_circle Flexible working hours and remote work opportunities.
• check_circle Work on advanced AI with real-world impact.
• check_circle Budget for home office equipment and productivity.
• check_circle Personal development budget and knowledge-sharing sessions.
• check_circle Newly designed and inspiring office environment.
• check_circle Competitive salary based on experience and qualifications.