{"id":3608800,"url":"https://alion.io/job/differential-bio-machine-learning-data-scientist-posting","title":"Machine Learning Data Scientist","company":{"id":2465863,"name":"Differential Bio","domain":"differential.bio","url":"https://alion.io/company/differential","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Munich, Germany"],"countries":["DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Bioreactors","optional":false},{"name":"Design Controls","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Time Series","optional":false},{"name":"Transfer Learning","optional":false},{"name":"Design of Experiments","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-11-02T11:01:37Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-04T17:06:40Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Role summary:\nAt Differential Bio, our mission is to unlock the potential of the bioeconomy by fixing how biological R&D and scale-up are done.\nWe’re looking for a Machine Learning Data Scientist to join our growing cutting-edge modelling team. You will develop novel machine learning algorithms and mechanistic models of bioprocess systems and push them from research proof-of-concept into production-ready commercial use.\nThis role is focused on research, including creating hybrid models that combine mechanistic and machine learning approaches to predict how microbial fermentations behave, and that keep working when we move to a new organism, a new sensor, or a larger reactor. You’ll play an instrumental role on two existing grants and work with leading academic groups and industrial partners on pilot- and production-scale data. In addition, you’ll develop new research projects and drive them to completion and application on customer projects.\nWe’re interested in researchers who want their work to end up in real bioreactors and are excited about working at an early-stage startup. You should be comfortable owning hard, open-ended problems where nobody has the answer yet, making evidence-based decisions under uncertainty, and shipping real solutions.\nHow you’ll work\nYou’ll collaborate with technical leads and academic/industry external partners to design and execute research that meets our commitments in both consortia and strengthens our own platform.\n\nYou’ll own your research projects from scoping to delivery, with support from our modelling lead but with autonomy and independence.\n\nYou’ll work closely with our wet lab, dry lab and software teams, and identify which data you need to generate from our robotic lab.\n\nYou can work remotely, provided you’re based within ±2 hours of Central European Time (CET), with occasional travel\n\nKey responsibilities:\nHybrid bioprocess modelling\nDevelop hybrid mechanistic/ML cell response models that predict fermentation outcomes (growth, titer, yield, productivity) from process and sensor data.\n\nFuse time-series signals from process analytical technology (Raman, NIR, dielectric spectroscopy, dissolved oxygen, off-gas) with offline analytics.\n\nBuild mechanistic backbones (e.g., kinetic/ODE models, genome-scale metabolic models) that constrain learning and generate synthetic training data.\n\nDevelop machine learning surrogate models for computationally costly mechanistic simulations.\n\nQuantify uncertainty so that models can be trusted in high-stakes production settings.\n\nTransfer across organisms and scales\nDesign models that carry over from benchtop to pilot and production scale, and across various microbes including bacteria and yeast.\n\nUse transfer learning and representation learning to make good predictions for new strains and processes with little data.\n\nDevelop scale-transferable reactor characterisations and predictive KPI models for scale-up.\n\nReal-time Model-Predictive Control\nBuild the AI/ML layer for Nonlinear Model Predictive Control (NMPC) and reinforcement-learning-based feeding strategies to optimize titer, rate, and yield dynamically.\n\nFuse real-time data from Process Analytical Technology (PAT) and soft sensors (e.g., Raman, NIR) into dynamic state estimators to drive closed-loop bioreactor actuators (feed, air, agitation).\n\nPrototype simpler, deployable steps along the way, such as deviation detection and operator alerts\n\nDesign control frameworks to proactively resolve metabolic bottlenecks and robustly account for model uncertainty, batch-to-batch variability, and inherent bioprocess stochasticity.\n\nQualifications:\nPhD in machine learning, bioengineering, computational biology, or a related field, or a Master’s degree with 3+ years of relevant industry experience.\n\nStrong research independence; ability to scope and execute novel scientific work from start to finish.\n\nDemonstrated evidence of research contributions, including (but not limited to): peer-reviewed journal or conference papers, conference attendance, open-source contributions.\n\nStrong Python and modern ML tooling (e.g., PyTorch/JAX, scikit-learn).\n\nHands-on experience building models of dynamical systems or time-series data, ideally combining mechanistic and data-driven approaches.\n\nExperience working with frontier coding models, including multi-agent orchestration.\n\nClear written and verbal communication, including with non-ML partners.\n\nNice-to-have:\nExperience with bioprocess or fermentation data, ideally from bioreactors at more than one scale\n\nExperience with Raman, NIR, or other spectroscopic data and chemometrics (e.g., PLS)\n\nBackground in model-predictive control, reinforcement learning, or optimal control\n\nExperience with transfer learning, domain adaptation, or representation learning on biological data\n\nFamiliarity with genome-scale metabolic models or kinetic modelling\n\nExperience with Bayesian methods, uncertainty quantification, or design of experiments\n\nExperience in collaborative or publicly funded research projects (e.g. Horizon Europe, Eurostars)\n\nBenefits:\nCompetitive compensation through salary + stock options; we are looking to build a small but mighty team and think about compensation along those lines;\n\nBuild your research profile: Work on open problems with leading European academic and industrial partners, with the intention to present and publish your work;\n\nA beautiful office in the heart of Munich, or remote work option with occasional travel\n\neGym WellPass membership","description_format":"text","description_chars":5509,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Stock options"],"hiring_locations":[{"name":"Germany","iso":"DE","kind":"country"},{"name":"France","iso":"FR","kind":"region"},{"name":"United 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