Machine Learning Scientist I/II - Multi-Modal Scientific Reasoning

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

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

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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 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 Impact at Lila Join our dynamic team in the Physical Sciences division as a Senior Machine Learning Engineer. In this pivotal role, you will be instrumental in shaping the infrastructure that empowers Lila’s scientific superintelligence to autonomously design, execute, and interpret intricate physics simulations. Your expertise will be critical in developing core systems that enable AI agents to reason over and control computational experiments, ranging from electronic structure calculations to surrogate-driven atomistic modeling, and beyond. Your contributions will directly enhance the capability of autonomous computational processes to explore chemical and materials landscapes with unmatched autonomy and robustness. What You Will Be Building Architect and implement agentic frameworks to support dynamic, multi-stage simulation workflows for scientific tasks. Develop pipelines that allow agents to autonomously plan, schedule, execute, and interpret computational tasks at scale. Create integration layers and APIs that connect machine learning models, large-scale simulation engines, databases, and heterogeneous compute platforms. Collaborate with AI researchers to productionize agent behaviors, including tool-use strategies, simulation-aware decision loops, and adaptive task planning. Enhance the robustness, modularity, performance, and reproducibility of agent-driven computational workflows; develop internal tooling for debugging, observability, and validation. What You’ll Need to Succeed A Master's degree or PhD in Computer Science, Machine Learning/AI, Scientific Computing, or a related technical field, or equivalent experience. Proven experience in building machine learning-driven pipelines, workflow systems, or tool-use frameworks, particularly for complex or scientific applications. Proficiency in Python and familiarity with machine learning ecosystems; experience with compiled languages such as C++, Rust, or Julia is advantageous. Understanding of large-scale scientific or engineering software, including integrating external tools into automated computational workflows. Background in distributed systems, high-performance computing environments, cloud platforms, or accelerator-based computing. A solid grasp of modern machine learning architectures and their deployment in production systems (e.g., GNNs, transformers, diffusion models, multimodal or tool-using models). Strong engineering fundamentals: reproducibility, testing, modular design, CI/CD, and scalable machine learning operations. Bonus Points For Experience developing or integrating agentic frameworks, autonomous machine learning pipelines, or multi-step tool-using agents, particularly for scientific applications.

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.

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.

We are seeking a dedicated and passionate Associate Scientist II to join our team in Cambridge. In this role, you will contribute to cutting-edge research and development initiatives, supporting our mission to innovate and enhance scientific solutions. You will work collaboratively with a multidisciplinary team to conduct experiments, analyze data, and interpret results, driving projects forward and contributing to significant advancements in our field.

Join our team at Integrated Resources, Inc. as an Associate Scientist II, where you will contribute to cutting-edge research and development in a dynamic and collaborative environment. In this role, you will utilize your scientific expertise to support various projects and drive innovation.