Principal Water Quality Scientist/Modeller

Your Impact at Lila Join our innovative Physical Sciences team as a Research Scientist, where you'll spearhead the development of cutting-edge generative modeling techniques to address significant challenges in materials science. Collaborate with a diverse team of machine learning experts, software engineers, and materials scientists to create and implement generative models tailored to Lila’s distinctive materials design needs. This role not only advances the forefront of scientific research but also allows you to witness the tangible impact of your work on materials being refined in our experimental facilities on a daily basis. What You’ll Be Building Generative Models: Create and deploy generative models, including Diffusion and flow-based models, alongside advanced sampling methods to solve diverse materials design challenges. Data Representation: Innovate architectures and approaches that incorporate physics-informed constraints and domain knowledge to model materials across a wide chemical space for real-world applications. Real-World Validation & Deployment: Develop and verify datasets, frameworks, and methodologies for validating generative models with experimentally realized materials. Collaborate with software engineers and product managers to implement these solutions. Cross-Functional Partnership: Engage with R&D leadership, product managers, and automation specialists to translate scientific inquiries into data requirements and modeling strategies. What You’ll Need to Succeed Proficient in Python and deep learning frameworks with experience in end-to-end workflow deployment. Familiarity with contemporary generative modeling techniques, including diffusion models and geometric deep learning, and their applications in scientific disciplines such as materials science, chemistry, and biology. A foundational understanding of materials science, physics, and chemistry, and the ability to incorporate these principles into generative model design. A self-motivated and independent thinker with keen attention to detail. Demonstrated experience in industry or notable academic achievements. Strong communication and presentation skills, with the ability to convey complex technical information effectively. Aspirations to collaborate with highly skilled teams in a dynamic, entrepreneurial, and technical environment.

Your Role at Lila Sciences As a key contributor to our Physical Sciences division, you will focus on advancing the field of in silico materials discovery techniques. Your responsibilities will involve designing autonomous workflows and data-driven pipelines that effectively integrate simulation with artificial intelligence. You will lead the development of strategies that empower agents to analyze simulation data, derive hidden insights, and facilitate hypothesis generation and materials design. Your innovative approach will enhance our utilization of simulation outputs for discovery, fostering a seamless integration of physics-based modeling with AI reasoning systems. Collaboration with specialists in simulation, AI agents, and experimental automation will be pivotal as we strive to redefine the landscape of digital discovery. Key Contributions Create cutting-edge workflows for in silico materials discovery that link physics-based simulations and generative AI models. Develop intelligent systems where AI agents autonomously design, execute, interpret, and optimize simulations. Establish frameworks that maximize the utility of simulation data for prediction, inference, and discovery, with features like automatic data extraction and model training. Prototype and assess novel paradigms for simulation-aware agents that learn from and interact with scientific simulations. Design data structures, metadata specifications, and APIs to ensure smooth information transfer among simulations, machine learning models, and experimental data repositories. Build scalable, modular workflows that connect electronic structure, atomistic, and mesoscale simulations with AI-driven reasoning and hypothesis generation. Work alongside computational scientists, machine learning professionals, and platform engineers to integrate in silico discovery pipelines into Lila’s overarching scientific superintelligence framework. Qualifications for Success PhD or equivalent experience in Computer Science, Materials Science, Chemistry, Physics, Applied Mathematics, or related fields. Strong background in in silico materials discovery, computational materials modeling, and/or simulation workflow design. Familiarity with large language models and their applications in scientific research. Experience in developing AI-driven or agentic workflows for scientific automation and discovery. Proficient programming skills in Python and scientific computing libraries.

Your Impact at Lila Join our dynamic and innovative AI safety team at Lila Sciences, where we prioritize talent and agency to mitigate risks associated with scientific superintelligence. Our mission is to craft and execute a tailored safety strategy that aligns with our unique objectives and deployment methods. This role involves creating technical safety strategies, engaging with the broader scientific community, and producing critical technical documentation, including evaluations focused on risk and capability assessments. What You’ll Be Creating Design and implement capability evaluations to assess scientific risks, particularly from cutting-edge scientific models integrated with automated physical laboratories across biological and physical sciences. Lead and coordinate threat modeling sessions with both internal and external scientific experts, keeping abreast of emerging technologies and use cases. Develop and manage high-quality training and testing datasets for evaluations and safety systems. Analyze risks associated with Lila’s capabilities and their interactions with the broader ecosystem of general-purpose frontier models and specialized scientific tools. Contribute to high-quality research initiatives focused on scientific capability evaluation and restriction as needed. Assist with external communications regarding Lila’s safety initiatives. What You’ll Need to Succeed A PhD in biological sciences (e.g., molecular biology, virology, computational biology) or physical sciences (e.g., materials science, physics, chemistry, or chemical engineering), or similar experience. Proficient in scientific computing related to biological or physical sciences. Familiarity with dual-use research and dissemination issues within relevant safety, regulatory, and governance frameworks (e.g., export control, biological and chemical conventions). Exceptional communication skills to convey complex technical concepts to non-expert audiences effectively. Proven ability to lead internal and external teams in developing Lila's perspective on biological and physical risks. Demonstrated capacity to collaborate with cross-functional stakeholders (science, AI, product, policy) in a complex environment.

About Lila Sciences Lila Sciences stands at the forefront of innovation with the world's first scientific superintelligence platform and autonomous laboratory dedicated to life sciences, chemistry, and materials science. Our mission is to revolutionize scientific discovery, leveraging AI to enhance every facet of the scientific method. By introducing scientific superintelligence, we aim to address humanity's most pressing challenges while accelerating advancements in human health, climate, and sustainability. Discover more about our vision at www.lila.ai. If you resonate with our mission and possess some of the qualifications listed below, we invite you to apply and join our dynamic team. Your Role and Impact at Lila As a Robotics Scientist at Lila, you will spearhead the research and development of autonomous robotic systems that constitute the intelligent backbone of our scientific superintelligence platform. Your innovations in novel algorithms and intelligent robotic solutions will enable seamless interactions between robotic systems and human scientists, driving fully autonomous workflows for groundbreaking scientific discovery. You will integrate cutting-edge robotics, machine learning, and systems engineering into your work, fostering a new era of scientific exploration. Your Contributions Will Include Designing and developing autonomous robotic systems for transport and workcell operations, incorporating sophisticated path planning, navigation, and motion planning algorithms. Creating production-level mobile manipulation platforms utilizing ROS/ROS2 and modular robotic architectures. Enhancing robotic perception by integrating diverse sensing modalities, including 3D vision, LIDAR, and tactile sensors, to facilitate robust and adaptable task execution. Employing simulation environments to model, test, and optimize task planning, scheduling, and robot behaviors across varied lab scenarios. Collaborating with AI, mechanical, and software engineering teams to transform theoretical robotics research into practical autonomous systems. What You Will Need to Succeed A Ph.D. in Robotics, Computer Science, Mechanical or Electrical Engineering, or a closely related field, or equivalent research experience. In-depth expertise in motion planning, path planning, and navigation for both manipulation and mobile robotics. Proficient in ROS/ROS2, C++, and Python, with practical experience in constructing robotic systems for real-world applications. Comprehensive understanding of perception systems and sensor integration, including cameras, LIDAR, and tactile sensors.

We are seeking a passionate and detail-oriented Scientist I to join our innovative team at Integrated Resources Inc. This entry-level position offers a fantastic opportunity to contribute to groundbreaking research and development projects. You will work closely with experienced scientists to support various laboratory activities, including experimental design, data collection, and analysis.

Your Role at Lila SciencesJoin our innovative Physical Sciences division, where you will spearhead the development of intelligent agent-driven systems designed to enhance AI-accelerated and AI-orchestrated process engineering across diverse industrial applications. In this pivotal role, your mission will be to create methodologies empowering AI agents to reason, design, simulate, optimize, and operate complex physical and chemical processes using both traditional and machine learning-driven process engineering tools. You will focus on building agentic infrastructures that facilitate AI systems in planning and executing multi-step process engineering workflows, which include process synthesis, flowsheet generation, steady-state and dynamic simulation, control strategy design, and techno-economic evaluation. Your contributions will significantly influence how Lila's scientific superintelligence addresses real-world challenges through closed-loop autonomous process engineering.Your ContributionsDesign and implement agentic frameworks supporting comprehensive process engineering workflows, encompassing process setup, simulation, optimization, and analysis.Create AI agents capable of autonomously planning, executing, and refining process engineering tasks utilizing existing tools such as steady-state and dynamic simulators, optimizers, and data systems.Investigate agentic strategies for advanced objectives, including process intensification, control co-design, real-time optimization, and closed-loop learning informed by operational data.Enhance the robustness, interpretability, and reproducibility of agent-driven process engineering workflows; develop internal tools for debugging, observability, validation, and auditability.Collaborate with interdisciplinary teams to apply agentic process engineering across a wide range of industrial applications.

AI Resident – 2026 CohortThe AI Residency Program presents a unique, full-time research opportunity aimed at connecting cutting-edge academic research with practical industry applications, specifically in the realm of AI for materials science. As a resident, you will collaborate closely with leading scientists and engineers at Lila Sciences on impactful, open-science projects, allowing you to either delve into fundamental research or apply innovative solutions to real-world challenges.Duration: 6–12 months (with the possibility of extension)Start Dates: Initial cohort members will begin in January 2026, with rolling applications and additional intakes scheduled for Summer and Fall 2026.Cohort Size: A select group of talented residentsMentorship: Dedicated pairing with technical mentors and constructive feedback from diverse cross-functional teamsResources: Access to proprietary datasets, high-performance computing resources, and Lila’s comprehensive research infrastructureResearch areas of focus include ML-accelerated simulations, Bayesian methods, representation learning, generative models, agentic science, and ML-driven automation.

Make an Impact at Lila Sciences As a pivotal member of our Physical Sciences division, you will spearhead the design and implementation of cutting-edge simulation methodologies aimed at modeling transport phenomena, kinetics, rare events, and reaction networks. Your innovative approaches will be integrated with artificial intelligence platforms to facilitate groundbreaking materials discovery. Your contributions will be essential in predicting, designing, and managing the behaviors of intricate materials and molecular systems, leveraging the power of agentic AI. Collaborating with our diverse teams, including experts in machine learning and materials science, you will help bridge theoretical research and practical experimentation. What You Will Create Enhance and develop molecular dynamics and Monte Carlo algorithms to effectively capture rare events, non-equilibrium processes, transport phenomena, and intricate reaction networks. Create scalable simulation workflows that merge statistical mechanics techniques with machine-learned interatomic potentials and agentic AI systems. Devise methods for synchronizing dynamic simulations with experimental observations to enable automated lab verification and discovery. Work collaboratively with computational scientists, machine learning specialists, and platform engineers to elevate the accuracy and scalability of simulation-driven material discoveries. Establish reproducible and modular software pipelines for statistical mechanics and dynamics simulations, optimized for high-performance computing and cloud environments. Qualifications for Success A PhD or equivalent research/industry experience in Physics, Chemistry, Chemical Engineering, Mechanical Engineering, Applied Mathematics, or related fields. A robust background in statistical mechanics, free energy calculations, reaction mapping, non-equilibrium dynamics, and rare-event sampling techniques. Proven expertise in molecular dynamics, Monte Carlo simulations, and/or kinetic simulation software frameworks (e.g., LAMMPS, GROMACS, OpenMM, HOOMD). Strong programming skills and experience in scientific computing (Python, C/C++, MPI, CUDA, etc.). Experience in executing and automating simulations on high-performance computing (HPC) and/or cloud platforms at scale. Bonus Qualifications A solid publication record showcasing the application of advanced statistical mechanics or dynamics simulations to molecular and materials systems, including molecular/biomolecular systems and solid-state materials and interfaces. Experience in integrating dynamics simulations with data-driven, AI-based, and/or agentic frameworks.

Your Contribution at Lila At Lila, we are assembling a highly skilled and proactive AI safety team that will collaborate with all core departments, including science, model training, and lab integration, to effectively address risks associated with scientific superintelligence. The primary mission of this team is to develop and execute a tailored safety strategy that aligns with Lila's unique objectives and deployment methodologies. This will encompass formulating technical safety strategies, engaging with the broader ecosystem, and producing technical documentation such as risk and capability assessments and safety measures. Your Responsibilities Establish the research and development strategy for Lila’s safety framework concerning biological and physical risks. Design and implement capability evaluations to identify scientific risks (both recognized and novel) arising from state-of-the-art scientific models integrated with automated physical laboratories across biological and physical sciences. Lead and coordinate threat modeling sessions with both internal and external scientific experts, including monitoring advancements in technologies and their applications. Create and curate high-quality training and testing datasets for evaluations and safety systems. Assess risks linked to Lila’s capabilities, considering interactions with the broader ecosystem of capabilities (including general-purpose frontier models and specialized scientific tools). Contribute to extensive, high-quality research initiatives when needed for scientific capability evaluation and restriction. Engage in external communications regarding Lila’s safety initiatives. Qualifications for Success A PhD in a biological sciences field (e.g., molecular biology, virology, computational biology) or a physical sciences field (e.g., materials science, physics, chemistry, chemical or nuclear engineering) or equivalent experience. Proven track record in setting research directions for open issues surrounding dual-use risks in biological and physical sciences. Experience in scientific computing within the biological or physical sciences. Understanding of dual-use research and dissemination issues in relation to relevant safety, regulatory, and governance frameworks (e.g., export controls, biological and chemical-related conventions). Excellent communication skills, capable of articulating complex technical concepts to non-specialist audiences. Demonstrated leadership capabilities in guiding teams of internal and external collaborators in developing Lila's perspective on biological and physical risks.

Role overview Lila Sciences seeks a Scientist I/II in Drug Delivery Chemistry for its Cambridge, MA location. This role centers on advancing drug delivery systems that align with the company’s broader research objectives. Key responsibilities Collaborate with fellow researchers to design and refine drug delivery technologies. Apply chemistry expertise to enhance scientific strategies and laboratory processes. Contribute to projects focused on improving patient outcomes through innovative drug delivery solutions. Location This position is based in Cambridge, Massachusetts.

Your Impact at Lila Join our team as a Machine Learning Scientist focused on pioneering multi-modal reasoning through vision-language models (VLMs) leveraging real-world scientific data, including figures, plots, and microscopy data from various sources. Your innovative designs will contribute to the advancement of Scientific Superintelligence. What You Will Be Building Lead cutting-edge research on multi-modal reasoning systems that analyze scientific data (images, plots, text, etc.) using advanced and custom VLMs. Design and implement training, adaptation, and test-time strategies (e.g., instruction tuning, supervised learning, RLHF, RAG) tailored for scientific comprehension tasks. Create datasets and benchmarks from authentic scientific artifacts (e.g., microscopy images, spectra, protocols) to evaluate model performance. Develop perception modules (e.g., OCR, table/structure recognition, plot parsing) for handling multi-modal data types. Collaborate with domain scientists and engineers to transition research into production-ready systems for enhancing scientific superintelligence. What You’ll Need to Succeed A graduate degree in a relevant discipline (Computer Science/AI, Applied Mathematics/Statistics, Electrical Engineering) or a physical sciences field (Materials, Chemistry, Physics) with a strong focus on machine learning; or equivalent research/industry experience. A proven track record in multi-modal machine learning or VLMs, evidenced by deployed systems, publications, or contributions to open-source projects. In-depth understanding of scientific QA/benchmarks and custom evaluation design. Experience with multi-modal fine-tuning, document parsing, dataset curation, and benchmarking. Robust engineering skills utilizing modern machine learning frameworks (e.g., PyTorch, Hugging Face). Strong communication and collaboration skills in cross-functional environments. Bonus Points For Experience with scientific data modalities in laboratory settings, such as microscopy images. Publications in leading ML/CV/NLP conferences or demonstrable impact in applied industrial research. Contributions to open-source multi-modal tools, evaluation suites, or datasets. About Lila Lila Sciences stands at the forefront of scientific superintelligence, operating as the world’s first platform and autonomous laboratory dedicated to life sciences, chemistry, and materials science. We are committed to revolutionizing discovery by harnessing AI to enhance every aspect of the scientific method.

Your Impact at Lila As a Machine Learning Scientist specializing in Scientific Reasoning, you will be at the forefront of advancing AI systems designed to think like scientists. Your role involves crafting innovative frameworks that extend the capabilities of LLM-based reasoning methods while also developing scalable systems that enhance Lila’s platforms. This position merges profound theoretical insights with hands-on ML engineering, facilitating breakthroughs in the generation, testing, deployment, and optimization of scientific hypotheses. What You Will Be Creating Develop and formalize frameworks for scientific reasoning using LLMs, incorporating structured prompting, reasoning chains, and computational strategies at test time. Investigate and implement techniques for in-context learning, self-reflection, and adaptive reasoning within scientific discovery processes. Create scalable model prototypes aimed at addressing cutting-edge scientific challenges. Collaborate with scientists and engineers to embed domain knowledge into reasoning systems that unify symbolic and statistical methodologies.

Your Impact at Lila The Scientist I/II, Process Chemistry will utilize cutting-edge process chemistry methodologies to enhance Discovery Chemistry initiatives across Lila’s diverse platform. This role emphasizes reaction and reactor engineering, process design, high-throughput experimentation, and digital chemistry workflows aimed at small molecules and associated molecular constructs. We seek a hands-on scientist who possesses a robust background in synthetic and process chemistry, complemented by an engineering mindset and experience in developing scalable, data-driven experimental workflows. Collaboration is key in this role; you will work closely with teams in Discovery Chemistry, analytical chemistry, automation, platform engineering, and AI/computational science to design, implement, and refine reaction and process workflows. These improvements will enhance the speed, reliability, and scalability of chemical transformations. The Scientist I/II will play a crucial role in bridging early-stage discovery chemistry with contemporary process development by establishing efficient, digitally enabled, and automation-friendly methods for reaction screening, process optimization, and chemistry execution. This position is perfect for a scientist passionate about the convergence of chemistry, engineering, high-throughput technologies, process intensification, and digital experimentation and eager to contribute to the evolution of next-generation process chemistry capabilities beyond traditional methods. What You Will Be Building Design, execute, and optimize process-relevant chemical transformations that advance Discovery Chemistry programs and the creation of small molecules. Develop reaction and process workflows with a focus on engineering rigor, scalability, reproducibility, throughput, and data integrity. Implement high-throughput experimentation (HTE) for reaction screening, process optimization, condition scouting, and assessment of process-relevant variables. Create and execute workflows that integrate reaction setup, process screening, workup, analytical readout, and data capture into streamlined experimental cycles. Contribute to the design of digitally enabled process chemistry workflows, including structured experimental data generation, electronic documentation, and the integration of chemistry data into computational and AI-ready systems. Collaborate with automation and platform teams to refine reaction and process workflows for robotics-enabled experimentation.

COMPANY DESCRIPTION Flagship Pioneering is a leading bioplatform innovation firm dedicated to inventing and building transformative companies that aim to make a significant impact on the world. By merging top-tier scientific talent with entrepreneurial visionaries, we mobilize the resources necessary to support bold innovations. Our endeavors in human health, sustainability, and beyond accelerate scientific advancements across a spectrum of fields, including disease detection and treatment, nature-positive agriculture, and cutting-edge AI applications. What distinguishes Flagship is our unique capacity to propel science and technology forward by integrating innovation, company formation, and capital investment under one roof—an approach that is largely unprecedented. Our scientific pioneers, entrepreneurial leaders, and expert capital managers are aligned in a structured process that facilitates transformative innovation for the benefit of both humanity and the planet. We have founded companies that tackle some of society's most pressing challenges, including delivering vaccines to billions during the COVID-19 pandemic, finding cures for chronic diseases, enhancing health outcomes, and fostering sustainable agricultural practices. Flagship Pioneering has been recognized twice on FORTUNE’s “Change the World” list, acknowledging our positive social and environmental contributions, and we have also been honored on Fast Company’s annual list of the World’s Most Innovative Companies. THE ROLE We are on the lookout for a Senior Scientist to spearhead the design, characterization, and optimization of aerosolizable particle systems. This pivotal role involves developing a comprehensive understanding of engineered particles during their generation, dispersion, and transport in air, and translating that knowledge into robust and scalable material and system designs. The Senior Scientist will operate at the intersection of materials science, aerosol physics, and experimental engineering. You will lead the development of aerosol testing capabilities, shape experimental strategies, and connect particle design with airborne performance. This individual will play a vital role in establishing core methodologies and driving projects from concept to validation. In this highly collaborative position, you will work closely with formulation, process development, and hardware teams to ensure seamless integration between particle design, production processes, and real-world deployment challenges.

Your Impact at Lila As an ML Research Scientist specializing in Multimodal Data Extraction, you will play a pivotal role in advancing Lila's mission of achieving scientific superintelligence. Your work will focus on the development of foundational models capable of autonomously reading, interpreting, and organizing scientific knowledge from diverse formats such as text, images, and experimental data in the physical sciences. Your research will contribute to the unification of global scientific data into a machine-readable format, enhancing reasoning, prediction, and autonomous discovery within materials science and chemistry. What You Will Be Building Innovate and create AI systems that effectively extract and organize knowledge from a variety of scientific resources. Design and optimize large language models, multimodal models, and specialized architectures for accurate and interpretable data extraction. Develop scalable solutions for managing unstructured and heterogeneous scientific data, integrating various formats including text, tables, and visuals. Collaborate with subject matter experts to ensure that the extracted data aligns with real-world research workflows. Publish impactful research that propels the field of multimodal understanding and AI-driven knowledge extraction forward.

Your Impact at Lila In this pivotal role, you will spearhead the innovation and validation of advanced pooled assays utilizing mammalian cells. Your work will encompass everything from proof of concept to automated screening processes, enabling the generation of intricate, high-dimensional datasets. This position involves collaboration across cell biology, protein design, and nucleic acid teams to facilitate biologic development programs, focusing on the rapid assessment of binding, stability, function, and immunogenicity of diverse biologics. What You'll Be Building Create next-generation mammalian display-based assays for binding, stability, function, and immunogenicity. Implement cutting-edge multiomic, single-cell, and in situ sequencing workflows to design innovative, highly multiplexed assays. Collaborate with protein design, nucleic acid, and automation teams to leverage these workflows in biologics development. Closed-Loop Experimentation and Execution: Work closely with engineering and automation teams to architect automated, high-throughput experimentation platforms that yield data-rich workflows. Join forces with the AI team to conduct iterative experiments in real time, facilitating rapid optimization and discovery cycles. Collaborate, Lead & Communicate: Engage with cell biologists, synthetic biologists, automation engineers, protein design scientists, and data scientists to document systems, share insights, and refine best practices in autonomous science. Mentor junior scientists, align project milestones with internal and external collaborators, contribute to intellectual property and publications, and present findings across the organization. What You’ll Need to Succeed Ph.D. in Chemical/Biomedical Engineering, Biochemistry, Pharmaceutical Sciences, Immunology, or a related discipline (or M.S./B.S. with 5–8+ years of relevant industry experience). Proficiency in the development and/or application of mammalian display or similar pooled mammalian systems (perturb-seq, MPRA, etc.). Experience in designing, executing, and analyzing single-cell, in situ, or multiomic sequencing experiments. Expertise in creating complex libraries of defined sequences in mammalian cells. Experience in designing, executing, and analyzing flow cytometry and sorting experiments. Familiarity with next-generation sequencing (NGS) based experimental design and analysis.

Join our dynamic team as an Associate Scientist II, where you will contribute to innovative research and development initiatives. You will work collaboratively with cross-functional teams to advance scientific projects and support our mission to drive breakthroughs in biotechnology.

Your Impact at Lila As a Scientist I/II in Organic Chemistry, you will play a pivotal role in the development, execution, and optimization of cutting-edge chemical transformations that enhance Discovery Chemistry initiatives across Lila Sciences. Your focus will be on the rapid synthesis of small molecules and a diverse range of chemical compounds. This position is tailored for an experimental organic chemist who possesses a robust foundation in synthetic chemistry, complemented by practical experience in high-throughput experimentation (HTE), reaction workup, and analytical characterization. Collaborating closely with teams specializing in Discovery Chemistry, analytical chemistry, automation, screening, and AI/computational methods, you will aid in constructing and implementing efficient chemistry workflows designed to expedite molecular design–make–test–analyze cycles. The Scientist II will be integral to reaction development, analog generation, and route execution utilizing contemporary organic chemistry techniques that are compatible with scalable, data-driven, and increasingly automated discovery environments. This role is ideal for a chemist passionate about addressing synthetic challenges at the intersection of reaction innovation, high-throughput experimentation, process efficiency, and analytical insight, and who is driven to contribute to the evolution of next-generation discovery workflows that extend beyond conventional bench chemistry. What You'll Be Building Design and optimize chemical transformations pertinent to Discovery Chemistry programs, with a strong emphasis on the swift synthesis of small molecules and analog series. Utilize modern synthetic organic chemistry methodologies to facilitate efficient exploration of a wide spectrum of structurally diverse and medicinally relevant chemical spaces. Plan and execute multi-step synthesis, reaction optimization, workup, purification, and compound characterization to support molecular discovery campaigns. Develop and implement HTE workflows aimed at reaction screening, condition optimization, reagent evaluation, and rapid assessment of transformation scopes. Establish robust methodologies for reaction setup, workup, sample preparation, and analytical readout to support rapid and information-rich experimentation. Employ analytical tools such as chromatography, NMR, and other related methods to evaluate reaction performance, characterize products, and inform synthetic strategies. Contribute to the evolution of practical and scalable chemistry workflows that integrate reaction discovery, optimization, and downstream compound generation.

We are seeking a motivated and detail-oriented Scientist I to join our dynamic team in Cambridge. The ideal candidate will have a strong background in scientific research, with a passion for innovation and problem-solving. This entry-level role offers an exciting opportunity to contribute to groundbreaking projects and advance your career in a collaborative environment.