Machine Learning Research Scientist I/II - Multimodal Data Extraction

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 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.

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 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.

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.

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.

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 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.

We are seeking a highly motivated and detail-oriented Research Scientist to join our innovative team in Cambridge. In this role, you will conduct groundbreaking research, analyze data, and contribute to the development of new solutions in our field. You will work collaboratively with cross-functional teams to drive projects from ideation to execution, ensuring scientific rigor and excellence.

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 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 seeking a highly motivated and detail-oriented Research Scientist to join our innovative team in Cambridge. As a key member of our research department, you will be responsible for conducting cutting-edge experiments, analyzing data, and contributing to groundbreaking discoveries in your field. You will collaborate with a diverse group of scientists and researchers to drive projects that align with our mission of advancing scientific knowledge.

Join our team at Integrated Resources Inc. as an Associate Research Scientist, where you will contribute to cutting-edge research projects that impact real-world applications. We are looking for passionate individuals eager to innovate and collaborate in a dynamic environment. In this role, you will support senior scientists in the design and execution of experiments, analyze data, and contribute to publications. This is a fantastic opportunity to grow your career in scientific research.

Join Integrated Resources Inc. as an Associate Scientist I, where you will contribute to innovative scientific research and development projects. As a pivotal part of our team, your role will involve conducting experiments, analyzing data, and collaborating with multidisciplinary teams to advance our scientific objectives.

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 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 us at the Toyota Research Institute (TRI) as we strive to enhance the quality of human life through innovative technologies. Our mission is to create groundbreaking tools that enrich human experiences. To spearhead this transformative evolution in mobility, we've assembled a stellar team that is pushing the boundaries in artificial intelligence, robotics, driving, and material sciences.The TeamWithin TRI's Energy and Materials division, the Future Factory team is dedicated to pioneering advanced tools and methodologies that drive flexibility and efficiency in Toyota's product design and manufacturing processes. Our goal is to expedite the journey towards an emissions-free future. We are developing comprehensive AI systems capable of reasoning through the creation of physical objects — from initial design concepts to the assembly of actual components — and building the necessary infrastructure to train and evaluate these systems on a large scale.The OpportunityWe are seeking a Research Scientist to help us build intelligent systems for physical assembly. This role is an excellent fit for recent PhD graduates with a proven track record in implementation and a deep curiosity about the manufacturing process.As a member of our research team, you will design and implement learning pipelines from the ground up, conduct experiments to assess various architectural, data, and algorithmic alternatives, and influence the application of modern machine learning to the challenges of robotic assembly. Your work will intersect policy learning, reinforcement learning, and physical reasoning, allowing you to explore the integration of large language models and agentic infrastructure in solving real-world manufacturing challenges.

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