Scientist/Sr. Scientist, AI Safety

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.

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.

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.

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 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.

Join AbbVie, a global biopharmaceutical company, as a Senior Scientist I in our Neuroscience division specializing in Biologics Screening. In this role, you will lead innovative research efforts to develop therapies that address unmet medical needs in the field of neuroscience.

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.

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.

We are open to hiring for this role at various levels of expertise. For the right candidate, this position can be structured as a Senior Research Manager, with compensation tailored to experience and the anticipated scope of work, potentially exceeding the listed pay rate.About the Cambridge Boston Alignment InitiativeThe Cambridge Boston Alignment Initiative (CBAI) is a nonprofit research organization dedicated to promoting research and education aimed at facilitating a safe and beneficial transition to advanced AI systems. Our efforts include generating original research and accelerating AI safety initiatives through our fellowship programs.Our first summer fellowship cohort has already published significant papers at the Mechanistic Interpretability Workshop at NeurIPS and had accepted papers at ICLR. Additionally, some fellows have transitioned to roles at Goodfire and Redwood Research. Following a successful launch in 2025, we are poised for rapid expansion in 2026, with plans to host multiple fellowship cycles (Fall, Spring, and Summer), double our fellowship cohort, and quadruple our team size.Refer candidates to us and earn $5,000 if they are hired.The RoleIn this role, you will collaborate closely with research fellows and their esteemed mentors—renowned researchers from Cambridge and beyond—to support pioneering work on interpretability, AI control, formal verification for provably safe AI, evaluations, and various aspects of AI governance and policy. We are looking for research managers with experience in both technical research and governance and policy research.Research Management Responsibilities (0.7 FTE)Conduct regular one-on-one meetings with fellows to provide constructive feedback on research progress, assist in overcoming challenges, and coach them through issues such as debugging research methodologies and preparing literature scaffolds, as well as supporting data collection, analysis, and methodology development for experiments and hypothesis testing.Offer feedback on fellows' research and help cultivate an environment that encourages rigorous approaches among them.Connect fellows with relevant resources, literature, and opportunities available during and after the fellowship program.Facilitate communication between fellows and their mentors to ensure a supportive research ecosystem.

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.

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.

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.

Join the Cambridge Boston Alignment InitiativeThe Cambridge Boston Alignment Initiative (CBAI) is a nonprofit organization dedicated to pioneering research and educational initiatives aimed at ensuring a safe and beneficial transition to advanced AI systems. Our mission focuses on producing original research and accelerating AI safety through comprehensive fellowship programs.Since our initial summer fellowship cohort, we have achieved significant milestones, including published papers at prominent conferences such as NeurIPS and ICLR. As we enter 2026, we are poised for rapid growth, planning multiple fellowship cycles and expanding our team significantly.Refer candidates to us, and if hired, you will receive a $5,000 referral bonus!Your RoleAs a Research Program Associate, you will collaborate closely with Research Managers, mentors, and program leadership to design and refine the frameworks that empower fellows to excel in their research. This is a pivotal program-building position where you will create systems for mentor matching, research goal tracking, progress assessment, and problem-solving support for fellows.Program Design & Development (0.6 FTE)Enhance CBAI's fellow selection process and program deliverables.Identify effective outreach channels and manage outreach campaigns for future iterations.Develop evaluation frameworks to assess fellow progress and program effectiveness.Implement structural improvements based on feedback from fellows, mentors, and research managers.Assist in the planning and execution of fellowship events, such as speaker series and poster days.Fellow & Mentor Experience (0.4 FTE)Design and oversee the onboarding process for mentors, ensuring a positive experience.

Our MissionAt rai, we are dedicated to addressing the most pressing and foundational challenges in Artificial Intelligence and Robotics. Our goal is to pave the way for future generations of intelligent machines that enhance our daily lives.Position OverviewWe are seeking passionate and innovative Research Scientists with substantial hands-on research experience in one or more of the following areas: Cognitive AI, Athletic AI, Organic Hardware Design, or Robot Ethics. If you're enthusiastic about advancing robotic technology and its applications to improve functionality and effectiveness, we invite you to join our team!

Join our team as a Biology Scientist, where you will engage in groundbreaking research and development in the field of biology. You will collaborate with a talented group of scientists and contribute to innovative projects that aim to advance our understanding of biological processes. This is an exciting opportunity to be part of a dynamic environment that fosters creativity and scientific inquiry.

About Graphcore At Graphcore, we are pioneering the future of artificial intelligence computing. Our team comprises semiconductor, software, and AI specialists with extensive expertise in developing the complete AI compute stack—from silicon and software to large-scale infrastructure. As a proud member of the SoftBank Group, we benefit from substantial long-term investments, enabling us to contribute essential technology to the rapidly evolving SoftBank AI ecosystem. To capture the immense potential of AI, Graphcore is expanding globally, uniting the brightest minds to tackle the most challenging problems, where every individual is empowered to make a significant impact on our company, our products, and the future of AI. Job Summary As a Research Scientist at Graphcore, you will play a vital role in advancing AI research by exploring innovative ideas that address significant AI/ML challenges. The evolution of AI has been primarily driven by specialized hardware over the past decade, and we believe that developing hardware-aware AI algorithms and AI-optimized hardware will remain crucial for progress in this exciting domain. We seek candidates who are not only curious scientists but also proficient engineers, equipped with both theoretical knowledge and practical skills essential for impactful AI research. We welcome applicants with experience in low-power, edge, and embodied AI applications, including robotics, autonomous vehicles, and augmented/virtual reality. Your expertise will contribute to the training and deployment of multimodal AI models in these contexts, focusing on areas such as world models, real-time computer vision, and reasoning over audio and video streams. The Team The Graphcore Research team engages in both fundamental and applied research to define the computational needs of machine intelligence and showcase how hardware advancements can lead to the next generation of innovative AI models. We actively publish in leading AI/ML conferences (NeurIPS, ICML, ICLR) and participate in specialized workshops while collaborating with various research teams and organizations globally. We take pride in fostering a supportive and collaborative environment, where we organize ourselves around individual research interests to collectively solve challenges in domains such as efficient computation, model scaling, and distributed training and inference of AI models across multiple modalities and applications, including sequence and graph-based data. Our teams are spread across London, Cambridge, and Bristol, with projects and discussions that involve all locations.

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.

Your Impact at Lila As a Machine Learning Engineer on the Physical Sciences team, you will play a pivotal role in developing and managing comprehensive, scalable machine learning workflows. These workflows will address a wide range of scientific challenges in materials science, chemistry, and physical sciences. Your contributions will be instrumental in advancing research initiatives focused on cutting-edge algorithms, driving towards the establishment of scientific superintelligence to tackle today’s most significant challenges in physical sciences. What You Will Build Design, implement, and sustain end-to-end ML pipelines, encompassing data ingestion, feature engineering, model training, evaluation, deployment, and monitoring. Productionize models and services while ensuring robust testing, observability, and documentation in collaboration with cross-functional software teams; develop CI/CD workflows and automated evaluations to facilitate safe and frequent releases. Work closely with domain scientists and platform engineers to translate research insights into high-performing, scalable systems. Participate in technical design reviews, establish coding standards, and mentor colleagues on best practices. What You’ll Need to Succeed BS, MS, or PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience. A solid foundation in Python software engineering, including testing, packaging, and typing; experience with machine learning frameworks such as PyTorch and Hugging Face. Experience deploying ML services in cloud-based environments (FastAPI/GRPC, containers, orchestration, cloud infrastructure). Hands-on experience with deploying models in production systems (LLMs, multimodal models, databases, RAG) along with strong debugging and profiling skills. Effective communication and collaboration in cross-functional settings. Bonus Points For Familiarity with scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks. Experience in GPU optimization (CUDA, Triton, compilation, distributed training). Previous contributions to open-source ML or scientific software. Experience with workflow orchestration, data provenance, or large-scale computing environments. About Lila Lila Sciences stands as the pioneering platform for scientific superintelligence, offering an autonomous laboratory dedicated to life sciences, chemistry, and materials science. We are at the forefront of a new era of limitless discovery, harnessing AI to revolutionize research and innovation in these fields.