Senior Deep Learning Engineer (m/f/d)

Job description

Our Mission At Omegga, we're on a mission to reinvent how AI and spectroscopy can drive positive change for animals, people and the planet. We started with a clear objective: eliminate chick culling on a global scale through early, non-invasive in-ovo sex detection. This outdated practice still impacts billions of lives, and we're here to change that. We're not building for the niche, we're building for the global standard. Today, our systems are already in use, and the focus is on scaling performance, robustness, and deployment across an international customer base. For us, scaling means more than growth. It means changing an industry for the better. We are looking for people who take ownership, think in systems, and want to help turn a working product into the industry benchmark. If that sounds like you, we'd love to hear from you. What We Are Looking For We are looking for a Deep Learning Engineer with strong research depth and hands-on implementation skills to push our model performance forward. You will work on deep learning architectures and iterate rapidly: from hypothesis and experiments to evaluation, monitoring, and integration into our algorithm stack. Your work will directly improve detection quality and robustness in a real-world, noisy environment. Your Mission: What You'll Do Advance our deep learning models through research iteration: develop and test architectural improvements, training objectives, and optimization techniques. You will own the loop from idea → experiment → conclusion → next iteration. Build rigorous evaluation and benchmarks: define evaluation sets, establish clear metrics (precision, recall, accuracy, calibration), and create repeatable benchmark runs so improvements are measurable and comparable over time. Own monitoring of model quality: set up monitoring for model performance and data shifts, define alerting signals, and build lightweight reporting that makes regressions visible early. Partner cross-functionally to turn findings into impact: work with data/engineering teams to improve datasets and labeling strategies, and with product/ops stakeholders to align on what “good” looks like in practice. Your Profile: Qualifications & Requirements MSc in Computer Science or a related field with 4+ years of applied deep learning experience, or a PhD — paired with a proven track record of taking research from idea to working sy…