Senior Research Engineer, Mechanical Intelligence in Multimodal Models

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.

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.

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.

Are you passionate about advancing the field of machine learning? Join our team at Altos Labs as a Machine Learning Scientist or Senior Machine Learning Scientist. In this role, you will leverage your expertise to drive innovation and development in cutting-edge research projects. Collaborate with a multidisciplinary team to push the boundaries of machine learning technology.

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 Contribution at Lila Sciences As a Robotics Scientist at Lila Sciences, you will spearhead the research and innovation of autonomous robotic systems that form the intelligent physical backbone of our cutting-edge scientific superintelligence platform. You will be instrumental in developing groundbreaking algorithms and deploying advanced robotic solutions that engage seamlessly with human scientists and intricate laboratory settings. Your efforts will propel our mission forward by establishing fully autonomous workflows for scientific exploration, merging state-of-the-art robotics, machine learning, and systems engineering. Projects You Will Undertake Innovating advanced methodologies for precise and dexterous robotic manipulation that utilize foundation models, reinforcement learning, diffusion-based techniques, and human input, enabling adaptable and intelligent robotic systems capable of executing complex tasks in a variety of scientific environments. Creating novel frameworks for human-robot interaction that integrate imitation learning and learning from human feedback, demonstrations, and corrections, fostering intelligent robotic agents that can smoothly blend into human scientific workflows and swiftly adapt to new experimental scenarios. Enhancing dexterous manipulation research through avant-garde machine learning techniques, including diffusion models and adaptive learning algorithms, synthesizing multi-modal sensing (tactile, visual, and linguistic) to develop generative skill representations and sophisticated motor learning policies for intelligent robotic systems. Designing autonomous robotic systems equipped with trust calibration mechanisms, allowing intelligent agents to dynamically modify their behaviors based on contextual information in challenging scientific tasks.

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.

Innovate and Create Models from Scratch! At Flagship Pioneering, we are dedicated to launching pioneering companies that challenge the status quo. Within our Flagship Labs, small, agile teams develop new technical theses, rigorously test them, and build ventures around groundbreaking concepts. We are assembling an exceptional machine learning team within our newly established venture, Flagship Labs 120. Our focus is on uncovering hidden structures from complex measurement data of intricate physical systems, often necessitating mechanism-informed modeling, carefully designed inductive biases, and principled methodologies for inverse problems. This is a pioneering role dedicated to innovative modeling, moving beyond standard optimization tasks. You will have the opportunity to design, prototype, test, and refine novel approaches that establish the technical foundation of our platform starting from day one.

ABOUT PIONEERING INTELLIGENCE Pioneering Intelligence is a forward-thinking initiative that builds upon Flagship Pioneering's rich history of establishing groundbreaking scientific and computational enterprises. By leveraging cutting-edge advancements in artificial intelligence, machine learning, and data science, we aim to accelerate fundamental research and cultivate a dynamic portfolio of AI-first companies. As an integral part of Flagship's unique model that intertwines science, entrepreneurship, and investment, we transform revolutionary concepts into impactful companies, enhancing AI innovations that contribute to human health, sustainability, and more. THE ROLE We are on the lookout for a Principal Scientist specializing in Embedded Machine Learning and Computational Methods to spearhead various AI/ML and computational initiatives across early-stage ventures within our company origination framework. Your responsibilities will include defining and executing practical AI strategies, overseeing the development of methodologies and platforms (including systems design, drug design, molecular modeling, systems biology, protein design, and LLM-based workflows), and ensuring a high standard of rigor in model development, benchmarking, scaling, and reporting. You will also manage cross-functional teams as necessary, influence the strategic direction of our initiatives, and represent Pioneering Intelligence to venture teams and external collaborators. The ideal candidate is a self-motivated individual with a diverse skill set, capable of transitioning seamlessly from protein design to mass spectrometry or docking pipelines, and then creating LLM-based agents to streamline scientific workflows.

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.

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.

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.

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.

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.

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 Role at Lila Sciences We are seeking a talented Senior Software Engineer to collaborate with our Machine Learning Engineers and Researchers. You will be instrumental in developing software that enhances Lila’s ML workflows and research tools. Join a dynamic team of engineers as you contribute to the development, support, and maintenance of Lila’s cutting-edge ML libraries and tools. Your Contributions Create and optimize high-performance, secure, and thoroughly documented Machine Learning libraries that implement algorithms crafted by our machine learning specialists. Develop CI/CD pipelines and integration tests to streamline ML workflows. Design repository architectures that adhere to consistent standards. Assist with debugging, logging, and ongoing maintenance of Ray-based compute environments. Establish data ingestion pipelines connecting lab data with the ML teams. Qualifications for Success A minimum of 8 years of software development experience in commercial environments using Go or Python. Proven track record in implementing scalable software solutions. Familiarity with MLOps systems and GitOps tools (ArgoCD, GitHub Actions). Experience with orchestration frameworks like Ray, Argo, or Airflow. Strong knowledge in containerization, Kubernetes, and infrastructure-as-code tools. Excellent listening skills and the ability to comprehend complex problems and algorithms. Outstanding problem-solving abilities and a collaborative mindset. Self-motivated and detail-oriented, eager to work with dynamic, skilled teams in a fast-paced, entrepreneurial environment. Preferred Qualifications Experience with monitoring and logging tools such as Prometheus and Grafana. Background in research engineering or scientific software development. About Lila Sciences Lila Sciences is at the forefront of scientific innovation, pioneering the world’s first scientific superintelligence platform and autonomous laboratory for life sciences, chemistry, and materials science. We are committed to transforming the landscape of discovery by applying AI to every facet of the scientific method. Our mission is to leverage scientific superintelligence to address humanity's most pressing challenges, empowering scientists to deliver solutions in health, climate, and sustainability with unprecedented speed and scale. Discover more about our vision at www.lila.ai.

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.

We are seeking a talented and motivated Data Scientist to join our dynamic team at Integrated Resources, Inc. in Cambridge. As a Data Scientist, you will play a pivotal role in analyzing complex data sets, developing predictive models, and providing actionable insights that drive business decisions. You will leverage your expertise in statistical analysis and machine learning to help us enhance our products and services.Your contributions will directly impact our strategic initiatives and improve overall operational efficiency. If you are passionate about data and eager to make a difference, we invite you to apply and become part of our innovative team!

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.