Senior Scientist I, Neuroscience – Biologics Screening

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 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 dynamic and empowered AI safety team dedicated to proactively addressing the potential risks associated with scientific superintelligence. This team will collaborate closely with all core departments, including science, model training, and lab integration, to craft a customized safety strategy that aligns with our unique objectives and deployment methods. Key responsibilities will encompass the development of technical safety strategies, engagement with the broader ecosystem, and the creation of essential technical documentation, including risk assessments and capability evaluations. Your Key Responsibilities Design and execute evaluations to identify scientific risks—focusing on both established and emerging threats—from state-of-the-art scientific models integrated with automated physical laboratories. Develop initial proof-of-concept safety measures, such as machine learning models designed to detect and mitigate unsafe behaviors from scientific AI models and physical laboratory outputs. Gain a comprehensive understanding of various model capabilities, primarily within scientific contexts but also extending to non-scientific domains (e.g., persuasion, deception) to shape Lila's overarching safety strategy. Engage in high-quality research initiatives as needed to evaluate and restrict scientific capabilities effectively. Qualifications for Success A Bachelor's degree in a relevant technical field (e.g., computer science, engineering, machine learning, mathematics, physics, statistics) or equivalent experience. Proficient programming skills in Python and hands-on experience with machine learning frameworks (such as Inspect) for large-scale evaluations and structured testing. Demonstrated experience in constructing evaluations or conducting red-teaming exercises pertaining to CBRN/cyber risks or frontier model capabilities, encompassing both unsafe and benign attributes. Background in designing and/or implementing AI safety frameworks in cutting-edge AI enterprises. Exceptional ability to communicate intricate technical concepts and issues to audiences without technical expertise. Desirable Qualifications A Master’s or PhD in a field pertinent to safety evaluations of AI models within scientific areas or another technical discipline. Publications in AI safety, evaluations, or model behavior at leading ML/AI conferences (such as NeurIPS, ICML, ICLR, ACL) or model release documentation. Experience exploring risks arising from novel scientific advancements (e.g., biosecurity, computational biology) or utilizing specialized scientific tools (e.g., large-scale foundational models in science).

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.

Join Lila Sciences as a Senior Research Associate or Associate Scientist in the Protein Science department, focusing on our cutting-edge Antibody Platform. You will play a crucial role in advancing our research initiatives, contributing to the development of innovative antibody-based therapeutics. This position offers the opportunity to collaborate with a talented team dedicated to scientific excellence and the pursuit of transformative solutions in healthcare.

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.

Join our innovative team as a Senior Scientist where you'll drive critical research projects and make a significant impact in the field. We are seeking a highly motivated individual with a passion for scientific discovery and a strong background in relevant disciplines.

Our MissionAt Altos Labs, our mission is to rejuvenate cell health and resilience, aiming to reverse diseases, injuries, and the associated disabilities that arise throughout life.For further details, visit our website at altoslabs.com.Our ValuesWe embrace a singular value at Altos: Everyone Owns Achieving Our Inspiring Mission.Diversity at AltosWe hold a firm belief that diverse perspectives are crucial to scientific advancement and inquiry. At Altos, outstanding scientists and industry leaders collaborate from across the globe to further our shared mission. We prioritize creating a sense of belonging, ensuring that every employee feels valued for their unique contributions. We are collectively responsible for maintaining a diverse and inclusive workplace.Your Contributions to AltosIn the role of Senior or Principal Machine Learning Scientist, you will be instrumental in the development of generative AI/ML models for multi-modal, multiscale biology, spanning from virtual cells to agentic target assessment. We seek a hands-on, innovative, and collaborative individual to join our multidisciplinary team of scientists and engineers dedicated to transforming the treatment of aging and disease. The ideal candidate will flourish in a dynamic environment that values teamwork, transparency, scientific excellence, originality, rigor, and integrity.

Role overview Harvard University seeks a Principal Research Scientist for the Sciences - Chemistry and Chemical Biology department in Cambridge. This role leads research efforts, working closely with faculty and other researchers to advance scientific understanding in chemistry and chemical biology. What you will do Direct research projects in chemistry and chemical biology Collaborate with faculty and research teams on experimental design and analysis Apply innovative methods to address complex scientific questions Contribute to scientific publications and presentations

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!

ABOUT PIONEERING INTELLIGENCEPioneering Intelligence extends Flagship Pioneering's tradition of launching innovative scientific and computational initiatives. By leveraging the latest advancements in AI, machine learning, and data analytics, we aim to expedite fundamental research and build a diverse portfolio of AI-first companies. As a key player in Flagship's integrated approach to science, entrepreneurship, and investment, we transform groundbreaking concepts into transformative enterprises that enhance AI innovations in human health, sustainability, and more.THE ROLEWe are on the lookout for a Principal Scientist specializing in Embedded Machine Learning and Computational Techniques to spearhead various AI/ML and computational initiatives across early-stage ventures in our company origination process. You will be responsible for defining and executing practical AI strategies, overseeing the development of methodologies and platforms such as systems design, drug design, molecular modeling, systems biology, protein design, and LLM-driven workflows while ensuring the utmost rigor in model development, benchmarking, scaling, and reporting. You will manage cross-functional contributors as necessary, influence the strategic direction of the company, and represent Pioneering Intelligence to venture teams and external partners. The ideal candidate is a proactive, deep thinker who can seamlessly transition between protein design one week and mass spectrometry or docking pipelines the next, while also developing LLM-based agents to automate scientific workflows.

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.

ABOUT PIONEERING INTELLIGENCE Pioneering Intelligence continues the legacy of Flagship Pioneering by creating innovative scientific and computational ventures. Utilizing advancements in AI, machine learning, and data analytics, our mission is to expedite fundamental research and establish a diverse portfolio of AI-first companies. We integrate science, entrepreneurship, and capital to transform groundbreaking concepts into impactful companies, furthering AI advancements in human health, sustainability, and more. THE ROLE We are on the lookout for a Principal Scientist in Embedded Machine Learning/Computational to spearhead multiple AI/ML and computational projects within early-stage ventures as part of Flagship’s origination process. Your responsibilities will include defining and executing effective AI strategies, leading method and platform advancements (such as systems design, drug design, molecular modeling, systems biology, protein design, and LLM/agentic workflows), and ensuring excellence in model development, benchmarking, scaling, and reporting. You'll collaborate with cross-functional teams, shape the direction of the company, and represent Pioneering Intelligence to both venture teams and external partners. The ideal candidate is a proactive deep diver, capable of transitioning from protein design to mass spectrometry or docking pipelines, and then developing LLM-based agents to streamline scientific workflows.

Your Impact at Lila As a Senior or Principal Research Engineer specializing in Synthetic Data, you will play a pivotal role in shaping the vision, roadmap, and execution of our synthetic data initiatives. Your responsibilities will span from asset generation and simulation to integrating machine learning training and achieving measurable enhancements in model performance. Collaborating closely with our Research Engineering team, you will design, generate, and implement artificial datasets aimed at training, testing, and refining Lila’s platform to achieve our strategic objectives. What You Will Build Define and refine the synthetic data strategy along with a comprehensive multi-quarter roadmap. Create evaluation frameworks that effectively connect synthetic interventions with genuine model performance. Establish high standards for asset quality, diversity, thorough documentation, and reproducibility while fostering a robust review culture. What You Will Need to Succeed Over 6 years of experience in applied ML/ML systems, with at least 3 years leading industry initiatives, showcasing a strong track record in advanced algorithms and frameworks designed for large-scale synthetic data generation. More than 8 years of experience working with contemporary ML workflows, including Python, PyTorch, dataset tools, training loops, and evaluation frameworks; adept at profiling and optimizing GPU-intensive pipelines. Bonus Points For A proven history of constructing synthetic datasets from source data to significantly enhance model performance in specific domains. Experience with instruction fine-tuning and hill-climbing techniques. Ability to translate product requirements and feedback into a scalable synthetic data generation pipeline. Knowledge of quantization, distillation, routing, mixture-of-experts, and cost optimization at scale. Experience in compliance-heavy settings (HIPAA, PCI, FedRAMP) and with on-premises/VPC deployments. About Lila Lila Sciences stands at the forefront of innovation as the world’s first scientific superintelligence platform and autonomous lab, dedicated to life sciences, chemistry, and materials science. We are ushering in an era of limitless discovery by harnessing AI to enhance every facet of the scientific method. Our mission is to empower scientists to tackle humanity's most pressing challenges in health, climate, and sustainability at an unprecedented pace and scale. Discover more about our mission at www.lila.ai. If this sounds like an environment in which you would thrive, we encourage you to apply even if your experience doesn't perfectly align with every requirement listed.

Charles River Analytics, a proud member of the GRVTY family, is at the forefront of developing innovative solutions and advanced technologies designed to address some of the most challenging issues faced globally. Our dynamic team of tech entrepreneurs collaborates to enhance AI, robotics, intelligent sensing, and human-centered computing. The research and technology we develop not only propel government initiatives but also unveil new opportunities in the commercial sector. At Charles River, we take immense pride in cultivating a workplace that attracts and retains top-tier talent and creative problem-solvers in the industry. Join us as we strive to accelerate our mission-driven innovations!We are currently seeking a dedicated Senior/Principal Scientist with expertise in Human-Systems Integration, Human Factors, or Human-Computer Interaction. In this role, you will spearhead the development of cutting-edge tools and interfaces that empower users to effectively collaborate with technology in demanding environments.Your responsibilities will include leading and expanding teams of researchers, designers, and engineers to assess work domains and user needs, create novel human-AI interaction concepts, and evaluate interactive prototypes. You will also be responsible for defining and directing technical projects, while fostering robust relationships with clients, sponsors, and collaborators. As a subject matter expert in Human Systems, you will work alongside multidisciplinary teams to explore and pursue research opportunities across various technical domains.

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.