Qureos Mentor

Lila Sciences

<div>
 
 About Lila Sciences
 
 
 
 Lila Sciences is the world&rsquo;s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai
 
 
 At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.
 
 
 If this sounds like an environment you&rsquo;d love to work in, even if you only have some of the experience listed below, please apply.
 
 
 
 Your Impact at Lila
 
 
 
 As a Senior Scientist on our Physical Sciences team, you will lead the development of cutting-edge flow chemistry workflows. You&rsquo;ll design and optimize flow chemistry platforms that integrate real-time analytics, advanced control strategies, and AI-driven decision-making. In this role, you&rsquo;ll work closely with systems engineers and AI scientists to unlock new capabilities in autonomous chemistry and accelerate discovery at scale.
 
 
 
 What You'll Be Building
 
 
 
 <ul>
 <li>
 Design &amp; Commission: Architect and develop versatile flow chemistry platforms for autonomous synthesis, ensuring seamless integration with the Lila platform.
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 Integrate &amp; Scale: Build, control, and optimize flow reactors and inline analytics. Interface flow chemistry platforms with robotic and sensing systems.
 </li>
 <li>
 Workflow Optimization: Tune flow chemistry platform design and software pipelines for high-throughput closed-loop experimentation with rich metadata capture.
 </li>
 <li>
 Closed-Loop Execution: Collaborate with AI teams to run iterative experiments in real time, enabling fast optimization and discovery cycles.
 </li>
 <li>
 Collaboration &amp; Documentation: Work with chemists, material scientists, engineers, and data scientists to document systems, share insights, and refine best practices in autonomous flow chemistry.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Need to Succeed
 
 
 
 <ul>
 <li>
 Ph.D. or M.S. in Chemical Engineering, Chemistry, or a related field.
 </li>
 <li>
 &ge; 5 years of hands-on experience in flow chemistry, microfluidics, or microreaction engineering.
 </li>
 <li>
 Strong background in catalysis and organic synthesis.
 </li>
 <li>
 Experience with automation, instrumentation, and integrating real-time analytics into experimental workflows.
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 <li>
 Proficiency in Python for scripting, data analysis, and instrument control.
 </li>
 <li>
 Excellent problem-solving, communication, and collaboration skills.
 
 
 </li>
 </ul>
 
 Bonus Points For
 
 
 
 <ul>
 <li>
 Prior experience working in an autonomous or self-driving lab environment.
 </li>
 <li>
 Familiarity with cloud-based instrumentation control and orchestration.
 </li>
 <li>
 Hands-on experience with AI-driven experimental design or Bayesian optimization.
 </li>
 <li>
 Background in scaling workflows from proof-of-concept to routine, high-throughput operation.
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 <li>
 Contributions to open-source lab automation or scientific software projects.
 
 
 </li>
 </ul>
 
 We&rsquo;re All In
 
 
 
 Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
 
 
 
 A Note to Agencies
 
 
 
 
 Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science&rsquo;s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
 
</div>

Scientist / Senior Scientist, Flow Chemistry

<div>
 
 About Lila
 
 
 
 Lila Sciences is the world&rsquo;s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai
 
 
 If this sounds like an environment you&rsquo;d love to work in, even if you only have some of the experience listed below, we encourage you to apply.
 
 
 
 Your Impact at Lila
 
 
 
 Your role in our Physical Sciences division will center on developing the next generation of in silico materials discovery methods, from creating autonomous workflows and data-driven pipelines to building the interface between simulation and AI. You&rsquo;ll pioneer strategies that enable agents to reason over simulation data, extract latent insights, and guide hypothesis generation and materials design. Your work will expand how we leverage simulation outputs for discovery, accelerating the integration of physics-based modeling and AI reasoning systems. You&rsquo;ll collaborate with experts in areas spanning simulation, AI agents, and experimental automation to push the boundaries of digital discovery.
 
 
 
 What You'll Be Building
 
 
 
 <ul>
 <li>
 Develop methods and workflows for in silico materials discovery that connect physics-based simulations, generative models, and agentic AI systems.
 </li>
 <li>
 Build intelligent pipelines where AI agents can design, launch, interpret, and refine simulations autonomously.
 </li>
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 Design frameworks that utilize simulation data more effectively for prediction, inference, and discovery, including automatic feature extraction, model training, and data-driven exploration.
 </li>
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 Prototype and evaluate new paradigms for simulation-aware agents that can learn from and act on scientific simulations.
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 Design data representations, metadata standards, and APIs that enable seamless flow of information between simulations, machine learning models, and experimental databases.
 </li>
 <li>
 Create scalable, modular workflows that bridge electronic structure, atomistic, and mesoscale simulations with AI-driven reasoning and hypothesis generation.
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 Collaborate with computational scientists, machine learning experts, and platform engineers to integrate in silico discovery pipelines into Lila&rsquo;s broader scientific superintelligence ecosystem.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Need To Succeed
 
 
 
 <ul>
 <li>
 PhD or equivalent experience in Computer Science, Materials Science, Chemistry, Physics, Applied Mathematics, or related disciplines.
 </li>
 <li>
 Strong foundation in in silico materials discovery, computational materials modeling, and/or simulation workflow design.
 </li>
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 Familiarity with large language models and their application in scientific domains, and
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 Experience building AI-driven or agentic workflows for scientific automation and discovery.
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 Solid programming skills in Python and scientific computing frameworks
 </li>
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 Familiarity with atomistic simulation software and libraries (e.g., VASP, LAMMPS, ASE, Pymatgen, etc.).
 
 
 </li>
 </ul>
 
 Bonus Points For
 
 
 
 <ul>
 <li>
 Strong publication record in in silico materials discovery, simulation-AI integration, AI-driven inverse design.
 </li>
 <li>
 Familiarity with scientific agent architectures, large-scale reasoning systems, or multi-agent frameworks for hypothesis generation and experimental planning.
 </li>
 <li>
 Familiarity with ontologies, metadata standards, and data infrastructure for scientific simulations.
 </li>
 <li>
 Experience with automated experiment&ndash;simulation loops, integrating computational predictions with robotic or cloud-based laboratory platforms.
 
 
 </li>
 </ul>
 
 We&rsquo;re All In
 
 
 
 Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
 
 
 
 A Note to Agencies
 
 
 
 
 Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science&rsquo;s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
 
</div>

Research Scientist, In Silico Materials Discovery

<div>
 
 About Lila
 
 
 
 Lila Sciences is the world&rsquo;s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai
 
 
 If this sounds like an environment you&rsquo;d love to work in, even if you only have some of the experience listed below, we encourage you to apply.
 
 
 
 Your Impact at Lila
 
 
 
 Join our Drug Discovery group to build and deploy ligand-based AI that turns noisy, real-world assay data into decisive design guidance for hit-to-lead and lead optimization. You&rsquo;ll create QSAR models, retrosynthesis-aware generative design tools, and active-learning loops that partner with medicinal chemists to deliver better compounds, faster. This role complements our structure-based docking team by focusing on assay-driven, synthesis-constrained optimization&mdash;even when structures are uncertain or unavailable&mdash;ultimately accelerating DMTA cycles and improving candidate quality.
 
 
 
 What You'll Be Building
 
 
 
 <ul>
 <li>
 Ligand-based QSAR modeling: Develop multi-task and transfer-learned models for potency, selectivity, and developability (e.g., solubility, permeability, clearance, CYP/hERG, safety liabilities) using graph/message-passing, and conformer-aware features. Handle activity cliffs, applicability domain, and calibration for robust decision-making.
 </li>
 <li>
 Assay-driven hit triage and prioritization: Build models that learn from HTS, DEL, and follow-up assays; robust curve-fitting (4PL/5PL), plate/batch effect correction, dose&ndash;response QC, and time-split/scaffold-split evaluations to ensure prospective reliability.
 </li>
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 Closed-loop DMTA and MPO: Create active learning and Bayesian optimization strategies to propose the next best analogs under multi-parameter objectives (potency, selectivity, exposure, safety, IP). Incorporate uncertainty, diversity, and experimental cost to maximize information gain per cycle.
 </li>
 <li>
 Synthesis-aware design and retrosynthesis: Integrate template-based and template-free retrosynthesis with reaction prediction, condition and yield modeling, building-block availability, and cost/time/risk scoring. Make design suggestions that are directly makeable and prioritize routes compatible with internal/partner capabilities.
 </li>
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 Generative and enumerative libraries: Build BRICS/RECAP/fragment-linking enumerations and property-conditioned generative models (diffusion/RL/flow) that respect synthetic constraints and matched molecular pair (MMP) rules for local SAR exploration and scaffold hopping.
 </li>
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 SAR mining and explainability: Automate MMP analysis, local SAR maps, and substructure attributions to surface chemist-actionable insights; link assay deltas to specific modifications and highlight potential bioisosteres and de-risking moves.
 </li>
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 Data foundations: Establish cheminformatics pipelines for standardization (tautomer/salt/charge), deduplication, structure normalization, and assay/ELN/LIMS ingestion; define ontologies and metadata for traceability and reproducibility.
 </li>
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 Rigorous evaluation and deployment: Design leakage-safe splits (scaffold, temporal, series-aware), conformal prediction for calibrated decisions, and prospective tests. Ship APIs and tools that integrate with medchem workflows, procurement, and automated synthesis.
 </li>
 <li>
 Cross-functional partnership: Work closely with medicinal chemists, DMPK, biology, and automation to translate TPPs into modeling objectives and to operationalize model recommendations in real make&ndash;test cycles. Collaborate with structure-based colleagues to fuse physics- and assay-derived signals where beneficial.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Need to Succeed
 
 
 
 <ul>
 <li>
 Strong proficiency in Python and modern ML (PyTorch/JAX/TF, scikit-learn, XGBoost/CatBoost), with experience training at scale and deploying end-to-end pipelines.
 </li>
 <li>
 Deep experience in ligand-based modeling (QSAR/QSPR, multi-task learning, uncertainty and applicability domain, calibration) and ADMET prediction for medicinal chemistry.
 </li>
 <li>
 Solid grasp of medicinal chemistry principles: SAR development, bioisosteres, property tuning (pKa/logD/PSA), selectivity design, and liability mitigation (CYP, hERG, reactivity, permeability, solubility).
 </li>
 <li>
 Cheminformatics and data tooling: RDKit, Chemprop/DeepChem, conformer generation, fingerprints/descriptors, ELN/LIMS integration, and assay data processing/curve-fitting.
 </li>
 <li>
 Retrosynthesis and synthesis planning: Familiarity with template-based/template-free methods, route scoring, reaction/yield/condition prediction, building block catalogs, and makeability constraints.
 </li>
 <li>
 Active learning and design-of-experiments: Bayesian optimization, diversity sampling, and portfolio-aware selection under experimental and synthesis budgets.
 </li>
 <li>
 Ability to design rigorous, leakage-controlled benchmarks and prospective validations; experience with scaffold/time splits and activity-cliff-aware evaluation.
 </li>
 <li>
 Strong self-starter with excellent attention to detail and clear communication; able to collaborate tightly with chemists and biologists.
 </li>
 <li>
 Demonstrated industry experience or academic achievement.
 
 
 </li>
 </ul>
 
 Bonus Points For
 
 
 
 <ul>
 <li>
 PhD in Chemoinformatics, Medicinal Chemistry, Computational Chemistry, Computer Science, or related field with a strong publication record in ML/drug discovery venues.
 </li>
 <li>
 Experience building synthesis-aware generative models and integrating retrosynthesis into design loops; familiarity with tools like ASKCOS/AiZynth-style planners or equivalent.
 </li>
 <li>
 Track record improving DMTA cycle time and MPO outcomes in live programs; integration with procurement and automated synthesis platforms.
 </li>
 <li>
 Expertise with MMPA, activity-cliff handling, conformal prediction, and applicability-domain diagnostics in production.
 </li>
 <li>
 Experience triaging HTS/DEL data, PAINS/aggregator/covalent liability filters, and off-target/polypharmacology prediction.
 </li>
 <li>
 MLOps for cheminformatics: data versioning, experiment tracking, model serving/monitoring, and cloud/HPC scaling.
 
 
 </li>
 </ul>
 
 We&rsquo;re All In
 
 
 
 Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
 
 
 
 A Note to Agencies
 
 
 
 
 Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science&rsquo;s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
 
</div>

Machine Learning Scientist, Medicinal Chemistry & Lead Optimization

<div>
 
 About Lila Sciences
 
 
 
 Lila Sciences is the world&rsquo;s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai
 
 
 At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.
 
 
 If this sounds like an environment you&rsquo;d love to work in, even if you only have some of the experience listed below, please apply.
 
 
 
 Your Impact at Lila
 
 
 
 As a Scientist/Senior Scientist on our Physical Sciences team, you will design advanced synthesis platforms for the synthesis and characterization of zeolitic materials. Your work will be enabling high-throughput experimentation integrated with real-time analytics. Collaborating across chemistry, material science, machine learning, and data science teams, you&rsquo;ll help establish next-generation tools for advanced porous material development platforms.
 
 
 
 What You'll Be Building
 
 
 
 <ul>
 <li>
 Design &amp; Commission: Design and validate systems for synthesis of zeolitic and other types of porous materials with the help of our automation engineering team.
 </li>
 <li>
 Integrate &amp; Scale: Interface near real-time high throughput characterization tools with platforms to enable feedback-driven synthesis and formulation.
 </li>
 <li>
 Workflow Optimization: Integrate the synthesis platforms with robotics and software pipelines for automated high-throughput experimentation and data-rich workflows.
 </li>
 <li>
 Closed-Loop Execution: Collaborate with AI teams to run iterative experiments in real time, enabling fast optimization and discovery cycles.
 </li>
 <li>
 Collaboration &amp; Documentation: Work with chemists, material scientists, engineers, and data scientists to document systems, share insights, and refine best practices in autonomous porous material chemistry.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Need To Succeed
 
 
 
 <ul>
 <li>
 Ph.D. or M.S. in Chemistry, Chemical Engineering, Material Science or a related field.
 </li>
 <li>
 &ge; 5 years of hands-on experience in zeolitic material synthesis.
 </li>
 <li>
 Deep expertise in porous material characterization
 </li>
 <li>
 Experience in integrating analytical instruments into automated or modular experimentation platforms.
 </li>
 <li>
 Experience in high throughput synthesis and characterization techniques.
 </li>
 <li>
 Excellent troubleshooting, communication, and interdisciplinary collaboration skills; ability to both innovate and communicate cross-functionally across materials science domains.
 </li>
 <li>
 Experience in powder x-ray diffraction technique and data analysis (indexing, and refinement)
 
 
 </li>
 </ul>
 
 Bonus Points For
 
 
 
 <ul>
 <li>
 Proficiency in Python or other scripting languages for data analysis.
 </li>
 <li>
 Experience with self-driving lab platforms or closed-loop optimization systems.
 </li>
 <li>
 Knowledge of design-of-experiment (DoE) methods and Bayesian optimization.
 </li>
 <li>
 Experience contributing to scientific software tools or open-source automation frameworks.
 
 
 </li>
 </ul>
 
 We&rsquo;re All In
 
 
 
 Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
 
 
 
 A Note to Agencies
 
 
 
 
 Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science&rsquo;s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
 
</div>

Research Scientist, Porous Materials

Join Lila Sciences and explore 4   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Lila Sciences

ABOUT_COMPANY

Scientist / Senior Scientist, Flow Chemistry

Research Scientist, In Silico Materials Discovery

Machine Learning Scientist, Medicinal Chemistry & Lead Optimization

Research Scientist, Porous Materials