# R&D Machine Learning Engineer > DeepLab · Athens, Greece (Hybrid) · — · Posted 2026-07-15 **Workplace:** hybrid ## Description 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. ## Apply [Apply at DeepLab](https://apply.workable.com/deeplab/j/A75DCDA75D/apply) --- Powered by [Workable](https://www.workable.com)