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Experience Level
Entry Level
Qualifications
Master's or PhD in Computer Science, Engineering, or a related field. Strong background in machine learning, deep learning, and natural language processing. Experience with multimodal models and frameworks. Proficiency in programming languages such as Python or R. Excellent problem-solving skills and ability to work collaboratively in a team environment.
About the job
Bland Inc. seeks a Machine Learning Researcher specializing in Multimodal Large Language Models (LLMs) to join the team in San Francisco. The focus is on advancing AI systems that integrate language with other types of data.
Role overview
This position centers on research and development aimed at improving how AI models process and understand information from multiple sources, such as text combined with images or other modalities.
What you will do
Investigate how language interacts with additional data types within multimodal LLMs
Create and evaluate new methods to enhance AI model performance
Work closely with colleagues on projects designed to push the boundaries of machine learning
Location
This role is based in San Francisco.
About Bland Inc.
Bland Inc. is a forward-thinking technology company based in San Francisco, dedicated to advancing artificial intelligence through innovative research and development. We strive to create impactful solutions that enhance the way humans interact with technology.
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Search for Machine Learning Engineer Multimodal Foundation Models
The Bot CompanyAt The Bot Company, we are on a mission to create an innovative robotic assistant for every household.Our dynamic team, composed of talented engineers, designers, and operators, is based in San Francisco. We have a rich background from industry leaders such as Tesla, Cruise, OpenAI, Google, and Pixar, and we have successfully delivered products to hundreds of millions of users, honing our ability to create exceptional products and experiences.We pride ourselves on maintaining a streamlined team structure that fosters swift decision-making and minimizes bureaucracy. Each member is considered an Individual Contributor, granted substantial autonomy, ownership, and accountability. Our culture enables us to work across the technology stack with an emphasis on rapid iteration and execution.What We Seek in CandidatesCandidates for all positions at The Bot Company must exhibit remarkable sharpness and the capacity to thrive in high-pressure environments. We expect candidates to showcase:Exceptional Cognitive Abilities: You possess quick thinking, instant learning capabilities, and the ability to reason across diverse domains.Engineering Curiosity: You demonstrate an innate desire to understand how systems function, even beyond your area of expertise.Performance-Driven Attitude: You excel in fast-paced settings, effectively navigate ambiguity, and thrive under demanding circumstances.Machine Learning: Multimodal Foundation ModelsWe are developing unified foundation models capable of reasoning across text, images, video, and kinematics to inform intelligent robotic behaviors.You will engage with large-scale multimodal networks, overseeing the complete process from data handling to model training and deployment.Your ResponsibilitiesConstruct Native Multimodal Policies: Create architectures where vision, language, and other modalities are represented in a unified manner.Enhance Cross-Modal Reasoning: Explore and implement strategies to ensure that the model not only correlates modalities but also comprehends them (e.g., linking visual physics to kinematic constraints).Manage the Training Loop from Start to Finish: Design, execute, troubleshoot, and refine large-scale training experiments; identify failure points, enhance data mixtures, and tighten evaluations to achieve measurable improvements.Deploy and Refine Real Systems: Integrate models into practical robotic frameworks, enhance robot code for model deployment, and optimize performance for edge inference.
Join Prima MenteAt Prima Mente, we are pioneers in the field of biology-focused artificial intelligence. Our mission is to generate unique datasets, develop versatile biological foundation models, and translate scientific breakthroughs into real-world clinical applications. Our primary focus is on understanding the brain in-depth, safeguarding it from neurological disorders, and enhancing its function during health. Our dynamic team of AI researchers, experimentalists, clinicians, and operational experts are strategically located in London, San Francisco, and Dubai.Your Role: Foundation Models for BiologyAs a Machine Learning Engineer, you will be instrumental in the design, implementation, and scaling of foundational AI models and infrastructure for multi-omics at an unprecedented scale. Your contributions will facilitate significant advancements in scientific comprehension and lead to groundbreaking applications in the medical and biological fields.Key Responsibilities:Develop high-performance machine learning algorithms optimized for large-scale applications, ensuring utmost reliability and efficiency.Design, implement, and maintain comprehensive experimentation pipelines that allow for rapid iterations, precise assessments, and reproducible research results.Refactor and enhance prototype research code into clean, maintainable, and efficient repositories prepared for production-level deployments.Create fast data processing workflows that can effectively manage extensive datasets to expedite research and model development.Engage in experimental design, with a focus on high-impact experiments that yield the greatest signal-to-noise ratio.Growth ExpectationsIn 1 month, you will initiate initial experiments utilizing state-of-the-art machine learning models, review and apply advanced research papers, and enhance existing code for improved efficiency and precision.By 3 months, you will take ownership of a prototype model architecture, showcasing notable algorithmic enhancements, and contribute to methods for large-scale data ingestion and training.Within 6 months, you will have significantly impacted the implementation of a high-performance foundation model, incorporating key algorithmic optimizations that improve scalability and throughput, along with publishing internal benchmarks that demonstrate substantial effects.
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Our MissionAt Altos Labs, we are dedicated to restoring cell health and resilience through innovative cell rejuvenation techniques aimed at reversing diseases, injuries, and disabilities that can arise throughout life.For further insights, please visit our website at altoslabs.com.Our ValueOur singular Altos Value is: Everyone Owns Achieving Our Inspiring Mission.Diversity at AltosWe firmly believe that diverse perspectives are crucial for scientific innovation. At Altos, exceptional scientists and industry leaders collaborate globally to further our shared mission. We prioritize Belonging, ensuring all employees feel valued for their unique perspectives, and we hold ourselves accountable for maintaining a diverse and inclusive environment.Your Contributions to AltosAs a member of our team, you will accelerate and enhance our efforts in developing unified, multi-modal generative foundation models tailored for multiscale biology. You will be a key player in multidisciplinary teams that create the computational platforms essential for Altos to fulfill its mission.In this position, you will collaborate with other scientists and engineers across the Institute of Computation to design, develop, and scale cutting-edge foundation models that address biological inquiries and assist in discovering novel interventions for aging and disease. Your focus will be on synthesizing unstructured multimodal signals with structured relational data and knowledge graphs that depict biological realities.The ideal candidate will excel in a dynamic environment that values teamwork, transparency, scientific excellence, originality, and integrity.
Bland Inc. seeks a Machine Learning Researcher specializing in Multimodal Large Language Models (LLMs) to join the team in San Francisco. The focus is on advancing AI systems that integrate language with other types of data. Role overview This position centers on research and development aimed at improving how AI models process and understand information from multiple sources, such as text combined with images or other modalities. What you will do Investigate how language interacts with additional data types within multimodal LLMs Create and evaluate new methods to enhance AI model performance Work closely with colleagues on projects designed to push the boundaries of machine learning Location This role is based in San Francisco.
About Hike Medical Hike Medical is building the future of musculoskeletal care by combining advanced technology with practical healthcare solutions. Based in San Francisco’s Rincon Hill, the team develops a platform that spans three core areas: an AI-powered vision system for rapid web-based foot scans that generate custom 3D-printed orthotics, an AI agent platform that manages the entire DME workflow from intake through claims, and SoleForge, a high-scale 3D printing facility for custom medical devices. Hike Medical partners with some of the world’s largest employers and major orthotics and prosthetics organizations. Fortune 50 companies trust the platform to support employee well-being, and a broad network of clinical partners keeps the company connected to real-world needs. Custom insoles are just the starting point. The long-term goal is to reshape the industry with bionic devices: AI-designed, robotically manufactured orthotic and prosthetic products. The company aims to reach this milestone by 2040. Learn more at bionics2040.com. With $22 million raised across Seed and Series A rounds from leading investors, Hike Medical offers a results-oriented culture for those interested in the intersection of AI, manufacturing, and healthcare.
Full-time|On-site|San Francisco (London/Europe - OK)
Tavus – Multimodal AI Model OptimizationResearch EngineerAt Tavus, we are pioneering the human aspect of AI technology. Our objective is to make human-AI interactions as seamless and natural as in-person conversations, allowing for a human touch in areas that were once considered unscalable.We accomplish this through groundbreaking research in multimodal AI, focusing on human-to-human communication modeling (encompassing language, audio, and video) and the development of audio-visual avatar behaviors. Our innovative models drive applications ranging from text-to-video AI avatars to real-time conversational video experiences across sectors such as healthcare, recruitment, sales, and education.By empowering AI to perceive, listen, and engage with an authentic human-like presence, we are laying the groundwork for the next generation of AI workers, assistants, and companions.As a Series B company, we are supported by renowned investors, including Sequoia, Y Combinator, and Scale VC. Join us as we shape the future of human-AI interaction.The RoleWe are seeking an accomplished Research Scientist/Engineer with expertise in model optimization to be a vital part of our core AI team.The ideal candidate thrives in dynamic startup environments, is adept at setting priorities independently, and is open to making calculated decisions. We are moving swiftly and need individuals who can help navigate our path forward.Your MissionTransform state-of-the-art research models into fast, efficient, and production-ready systems through techniques such as sparsification, distillation, and quantization.Oversee the optimization lifecycle for critical models: establish metrics, conduct experiments, and evaluate trade-offs among latency, cost, and quality.Collaborate closely with researchers and engineers to convert innovative concepts into deployable solutions.RequirementsExtensive experience in deep learning with PyTorch.Practical experience in model optimization and compression, including knowledge distillation, pruning/sparsification, quantization, and mixed precision.Familiarity with efficient architectures such as low-rank adapters.Strong grasp of inference performance and GPU/accelerator fundamentals.Proficient in Python coding and adherence to best practices in research engineering.Experience with large models and datasets in cloud environments.Capability to read ML literature, reproduce results, and modify ideas accordingly.
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Join latentlabs, a pioneering company at the forefront of biotechnology, as we seek a talented Machine Learning Researcher specializing in generative modeling. You will become part of a dynamic, interdisciplinary team comprising machine learning experts, protein engineers, and biologists, all committed to revolutionizing biological control and disease treatment. In this role, you will design innovative generative models aimed at creating new proteins that exhibit functionality in wet lab assays.
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About Plaid Plaid builds tools that help developers create new financial products and experiences. Since 2013, Plaid has connected millions of users to over 12,000 financial institutions across the US, Canada, the UK, and Europe. The company partners with organizations like Venmo, SoFi, Fortune 500 firms, and major banks to make linking financial accounts to apps and services easier. Headquarters are in San Francisco, with offices in New York, Washington D.C., London, and Amsterdam. Team: Data Foundation & AI The Data Foundation and AI team designs and maintains the machine learning and AI infrastructure that supports Plaid’s products. This group transforms Plaid’s financial network data into flexible formats used by teams across the company. Responsibilities span the entire system lifecycle: data curation for pretraining, model development, deployment, serving, and monitoring in production. Role Overview: Senior Machine Learning Engineer (Research Scientist) This position focuses on applied research for Plaid’s foundation model. The Senior Research Scientist leads efforts to design model architectures, set pretraining objectives, and implement fine-tuning strategies that work across a range of product needs. The role also involves building and maintaining production machine learning systems, including training pipelines, model serving, feature engineering, and performance monitoring. Key Responsibilities Design model architectures and define pretraining objectives for Plaid’s foundation model Develop and apply fine-tuning methods for diverse product use cases Build and maintain end-to-end machine learning systems, from data pipelines to model serving Engineer features and monitor system performance in production Create evaluation frameworks to measure model quality across multiple tasks and metrics Location This role is based in San Francisco.
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About NomadicMLAt NomadicML, we are harnessing the power of artificial intelligence to revolutionize the way machines understand and interpret motion. Our vision-language models (VLMs) transform vast amounts of video data into actionable insights, paving the way for advancements in self-driving technology, robotics, and industrial automation.Founded by Mustafa Bal and Varun Krishnan, both alumni of Harvard University, our team is comprised of experts who have previously developed critical AI systems at industry giants like Snowflake, Lyft, Microsoft, Amazon, and IBM Research. With a commitment to innovation, we are dedicated to mining insights from the 5 trillion miles driven by Americans annually, uncovering the next frontier in machine intelligence.About the RoleWe are looking for a passionate Machine Learning Engineer who excels at the intersection of foundational model research and production engineering. In this role, you will play a key part in optimizing how machines learn from motion, focusing on training and refining large-scale Vision-Language Models that analyze complex real-world video data.You will be responsible for creating multi-modal architectures that accurately perceive, localize, and describe motion events across millions of video frames, transforming these innovations into robust APIs and SDKs for enterprise clients.Working closely with the founders, your contributions will include:Training and assessing VLMs tailored for motion comprehension within autonomous driving and robotics datasets.Designing and scaling GPU-accelerated pipelines for training, fine-tuning, and inference on diverse data types (video, language, and sensor metadata).Developing evaluation frameworks that benchmark spatiotemporal reasoning and localization precision.
The OpportunityJoin us at ComfyOrg as a Senior/Staff Applied Machine Learning Engineer! We are on the hunt for a passionate innovator who is enthusiastic about optimizing model inference. You will play a pivotal role in developing the heart of ComfyUI, our cutting-edge visual AI platform. Your expertise will help us push the limits of AI model performance, making them run faster and more efficiently than ever before.Are You a Match?You are fascinated by model inference, memory management, and torch optimizations.You possess experience in writing production-level PyTorch code that challenges performance standards.You have a passion for understanding the inner workings of AI models.You thrive on developing highly optimized code that consistently delivers results.You believe that the current landscape of ML deployment holds significant room for improvement.Your Responsibilities:Develop and enhance the core inference engine that drives ComfyUI.Optimize large models for speed and memory efficiency.Collaborate with our core team to architect new features.Tackle complex technical challenges within the visual AI domain.Contribute to the future direction of our technology.Experience with diffusion or LLM models, as well as creating custom nodes for ComfyUI, is highly beneficial.
Join Achira in shaping the future of deep learning with cutting-edge generative, representational, and simulation models for molecules and materials. Our mission is to create foundational models that render the atomistic universe understandable, predictable, and designable.Why Choose Achira?Be part of an elite, cross-disciplinary team comprising ML researchers, physicists, chemists, and engineers who are redefining atomistic simulation through expansive foundation models.Advance the integration of deep learning with the principles of nature, merging generative AI, probabilistic reasoning, and molecular physics.Engage in projects at an unparalleled scale, tackling extensive datasets, computational challenges, and ambitious goals.Take full ownership of your research journey — from ideation and architecture to training, evaluation, and deployment.Flourish in a dynamic culture that values rigor, speed, creativity, and impact over bureaucracy.Position OverviewAs a Generative AI Researcher at Achira, you will contribute to the development of foundation simulation models — large-scale systems designed to learn the structure, dynamics, and energetics of the atomistic realm. These models will unite deep representation learning, generative modeling, and sophisticated simulation techniques.Your responsibilities will include:Crafting and training state-of-the-art deep generative models — including diffusion, autoregressive, flow-based, and latent-variable architectures focused on molecules, materials, and atomic systems.Creating expressive representations of molecular and atomistic structures and dynamics utilizing equivariant graph neural networks, geometric transformers, and latent encoders that respect physical symmetries and constraints.Innovating advanced sampling and simulation techniques that blend probabilistic inference, deep learning, and reinforcement learning to facilitate efficient exploration and simulation of learned energy landscapes.Developing models that comprehend, generate, and simulate the physical world, merging reasoning, simulation, and predictive capabilities.Working collaboratively with physicists and chemists to validate models against ab initio, molecular dynamics, and experimental datasets.Rapidly prototyping, benchmarking, and iterating — converting research concepts into reusable, scalable model components across Achira’s foundation model suite.
Oct 24, 2025
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