Senior Machine Learning Engineer - Search & Recommendations

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 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.

Join the Team at GraphcoreAt Graphcore, we are at the forefront of revolutionizing AI computation. Our team of experts in semiconductors, software, and artificial intelligence is committed to developing an advanced AI compute stack that encompasses everything from silicon to datacenter infrastructure.As a proud member of the SoftBank Group, we are supported by significant long-term investment, enabling us to drive crucial technology within the rapidly expanding SoftBank AI ecosystem.To address the immense opportunities within the AI landscape, we are seeking to expand our teams globally. We welcome the brightest minds to tackle the most challenging problems, ensuring that every team member has an opportunity to influence our company, our products, and the future of AI.

Collaboration Fuels Innovation. Join Roku in Transforming TelevisionAs the leading TV streaming platform in the U.S., Canada, and Mexico, Roku aims to power every television globally. We pioneered the streaming revolution, and our mission is to connect the entire TV ecosystem. We empower viewers with access to their favorite content, assist content creators in reaching vast audiences, and offer advertisers unparalleled engagement tools.From day one at Roku, your contributions will be recognized and valued. In our rapidly growing public company, you won’t just observe; you will play a vital role in enhancing the experience for millions of TV streamers across the globe while gaining invaluable experience across various fields. About the TeamThe Advanced Development team at Roku is at the forefront of innovation, creating the next generation of intelligent and generative media systems. We explore concepts that are years ahead of production, crafting foundational technologies that will redefine content understanding, creation, and personalization across millions of Roku devices.This unique environment is comprised of a PhD-level, interdisciplinary team that merges machine learning research, software engineering, and DevOps. Our experts possess not only deep technical knowledge but also a broad creative vision, challenging the status quo, embracing uncertainty, and building unprecedented solutions. Our culture is collaborative, low-ego, and driven by ownership, trust, and curiosity.We seek an Applied Scientist with a robust foundation in mathematics, machine learning, and computer science, complemented by experience in cloud engineering, DevOps, and computer vision — someone who excels at the intersection of research and production.

Collaboration Fuels Innovation. Join Roku - Revolutionizing TV ViewingAs the leading TV streaming platform in the U.S., Canada, and Mexico, Roku is on a mission to empower every television worldwide. Pioneering the streaming experience, we connect viewers to their favorite content, support publishers in audience growth and monetization, and provide advertisers with exceptional tools for consumer engagement.From day one at Roku, your contributions will be valued. We are a rapidly expanding public company where every team member plays an essential role. This is your chance to impact millions of TV streamers globally while gaining significant experience across diverse disciplines. About Our TeamThe Advertising Performance team is dedicated to optimizing the performance of all participants in the advertising ecosystem, including advertisers, publishers, and Roku itself. Our systems and solutions leverage various disciplines and technologies for real-time, multi-objective optimization on a large scale with minimal latency. Utilizing Machine Learning, Reinforcement Learning, AI, and Optimization Systems, we tackle a wide array of complex challenges. Central to our efforts is our Machine Learning, Experimentation, and Inference Platform that supports the entire operational landscape.

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.

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 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.

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 Cambridge Mobile Telematics (CMT), the leading provider of telematics solutions worldwide, dedicated to enhancing road safety for drivers everywhere. Our innovative AI-driven platform, DriveWell Fusion®, aggregates sensor information from millions of IoT devices—including smartphones, connected vehicles, dashcams, and proprietary Tags—along with contextual data to deliver an integrated perspective on vehicle and driver behavior. Our insights empower auto insurers, automakers, commercial mobility firms, and government entities to enhance risk assessment, safety protocols, claims management, and driver improvement initiatives. With headquarters in Cambridge, MA and additional offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, we protect and monitor millions of drivers globally every day.CMT is seeking a talented and experienced Senior Software Engineer to contribute to the development and scalability of our on-device Machine Learning systems within the Mobile SDK. This pivotal team is responsible for the core C/C++ runtime that enables real-time sensor data processing and machine learning across a multitude of mobile devices. Our platform facilitates swift updates and deployment of driving behavior algorithms while ensuring optimal performance, battery efficiency, and operational accuracy.If you are a collaborative, customer-focused, and innovative individual eager to help us improve road safety by enhancing driver behavior, we invite you to apply!

About Graphcore At Graphcore, we are pioneering the future of AI computation. Our team consists of semiconductor, software, and AI specialists with extensive experience in creating a complete AI compute stack—from silicon and software to infrastructure at datacenter scale. As a proud member of the SoftBank Group, we are supported by significant long-term investments, allowing us to deliver vital technology to the rapidly expanding SoftBank AI ecosystem. To seize the immense and exciting opportunities in AI, Graphcore is actively expanding its teams globally, uniting the brightest minds to tackle the most challenging problems in an environment where everyone can significantly impact the company, its products, and the future of artificial intelligence. Job Summary In the role of Senior Machine Learning Engineer within the Applied AI team at Graphcore, you will play a crucial part in advancing AI technology by developing and optimizing AI models specifically designed for our specialized hardware. You will engage with large-scale systems where performance is paramount to the success of our initiatives. Collaborating closely with both the Software Development and Research teams, you will be instrumental in identifying innovative opportunities that set Graphcore’s technology apart. We are looking for engineers with robust technical skills and a deep understanding of large-scale AI model implementation, eager to make a meaningful impact in this fast-evolving field. The Team The Applied AI team's mission is to serve as advocates for our customers. We continually strive to understand the latest AI models, applications, and software to ensure that Graphcore’s technology integrates seamlessly with the AI ecosystem and operates efficiently at scale. Our responsibilities include building reference applications, optimizing key software libraries (including kernel efficiency on our hardware), and collaborating with the Research team to develop and publish innovative ideas across domains such as efficient computation, model scaling, and distributed training and inference of AI models across various modalities and applications. If you are passionate about advancing the next generation of AI models on cutting-edge hardware, we would love to hear from you!

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.

Location: Cambridge, MA (Eastern Time / UTC -4). Relocation support is available. Remote work may be considered for candidates based outside Massachusetts.Start date: ASAPLanguages: English (required) Pragmatike is an AI startup founded by MIT CSAIL researchers, recognized by GTM Capital as a Top 10 GenAI company. The team develops advanced AI systems with a focus on real-world applications. Role overview The Principal Machine Learning Operations Engineer shapes the architecture and scaling of Pragmatike’s machine learning infrastructure. This senior position leads the design, implementation, and optimization of production AI systems, overseeing the full lifecycle from model training and evaluation to deployment, monitoring, and ongoing improvement. Collaboration is central in this role. The Principal ML Ops Engineer works closely with AI researchers, GPU systems engineers, backend developers, and product teams. The main focus is building reliable, efficient, and automated ML platforms to support large-scale AI deployments. Key responsibilities Architect, build, and improve the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring. Design and maintain infrastructure for model deployment, version control, reproducibility, and orchestration across both cloud and on-premises GPU clusters. Optimize distributed systems for computational efficiency, covering Kubernetes, autoscaling, caching, GPU allocation, and checkpointing workflows. Establish observability for ML systems by monitoring model drift, performance, throughput, reliability, and operational costs. Automate workflows for dataset curation, labeling, feature engineering, evaluation, and CI/CD for ML models. Collaborate with researchers to bring models into production and improve training and inference pipelines. Set internal ML Ops standards, best practices, and develop tools that support cross-team collaboration. Mentor engineers and provide architectural guidance across the AI platform. Requirements Extensive hands-on experience designing and operating production ML systems at scale, ideally at the Staff or Principal level. Deep knowledge of ML Ops, distributed systems, and cloud infrastructure.

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.

Join Cambridge Mobile Telematics (CMT), the largest telematics service provider globally, dedicated to enhancing road safety and improving driver behavior. Our AI-driven platform, DriveWell Fusion®, integrates sensor data from numerous IoT devices, including smartphones, connected vehicles, and dashcams, to provide actionable insights for auto insurers, automakers, and public safety organizations. With headquarters in Cambridge, MA, and offices across the globe, we are committed to making a difference for millions of drivers every day.As a Principal Machine Learning Engineer on the innovative DriveWell Atlas team, you will lead the charge in developing cutting-edge AI systems leveraging telematics data. You will spearhead projects that involve the design, pre-training, fine-tuning, and deployment of novel foundation models aimed at revolutionizing risk assessment, driver engagement, and claims processing. This role demands a profound understanding of contemporary AI methodologies, machine learning, and sensor data analysis, offering the chance to construct a pioneering large language model from petabyte-scale datasets.We seek a collaborative and inventive Principal Machine Learning Engineer who is driven by a passion for making roads safer and improving driver capabilities.

Join Cambridge Mobile Telematics (CMT), the largest telematics service provider globally, dedicated to enhancing road safety for all drivers. Our innovative AI-driven platform, DriveWell Fusion®, aggregates data from millions of IoT devices—including smartphones, proprietary Tags, connected vehicles, and dashcams—fusing it with contextual information to provide a comprehensive view of driver and vehicle behavior. Our insights are utilized by auto insurers, automobile manufacturers, commercial mobility companies, and public sector entities to enhance risk assessment, safety protocols, claims processing, and driver improvement initiatives. With our headquarters in Cambridge, MA, and offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, we safeguard millions of drivers worldwide every day.As a Principal Machine Learning Engineer at CMT, you will craft algorithms that model the physical phenomena associated with vehicle and human movements as captured by mobile sensors and IoT devices. Your expertise will help solve complex real-world challenges and drive innovative solutions through exploratory data analysis on intricate, high-dimensional datasets. Leveraging your knowledge in statistics, machine learning, deep learning, signal processing, physics, programming, and data modeling, you will spearhead Data Science projects, independently create new solutions to meet CMT’s goals, and contribute to company-wide prototype development and product enhancements.If you are a collaborative, customer-focused, and inventive Principal Machine Learning Engineer eager to contribute to safer roads and better driving practices, we welcome your application!

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.

Join Luminance, a pioneering AI firm in the UK that is transforming the legal sector. Recognized in Tech Nation’s Future Fifty list and awarded with two Queen’s Awards, Luminance stands at the forefront of advanced AI technology.We are seeking passionate and dedicated candidates with a robust academic background in Artificial Intelligence or a related discipline to enrich our R&D team in Cambridge. As a member of our agile team, you will be instrumental in prototyping, developing, and executing innovative solutions to complex AI challenges, while assessing output quality, efficiency, and feasibility in a data-driven environment.This position is open to recent graduates eager to learn and seasoned professionals with substantial experience in the field.If you’re enthusiastic about this opportunity but feel your skills or experience don’t fully match the job description, we encourage you to apply. At Luminance, we are committed to fostering an inclusive workplace where everyone can thrive. Your enthusiasm may be the key to our search for the right candidate, whether for this role or another!

Join our dynamic team at ttp1 as a Simulation & Modelling Engineer - Consultant. In this role, you will leverage your expertise to develop innovative simulation models, engage in complex problem-solving, and provide valuable insights to our clients. You will collaborate with multidisciplinary teams to deliver high-quality engineering solutions that meet the needs of diverse industries.