Machine Learning Research Scientist - Quantum Accelerated Generative Models

About Sygaldry TechnologiesSygaldry Technologies is at the forefront of innovation, developing quantum-accelerated AI servers designed to significantly enhance the speed of AI training and inference. By merging quantum computing with AI, we are navigating the challenges of increasing compute costs and energy constraints, paving the way towards superintelligence. Our AI servers leverage a diverse range of qubit types in a fault-tolerant architecture, achieving the necessary balance of cost, scalability, and speed for advanced AI applications. We are committed to pioneering new frontiers in physics, engineering, and AI, tackling the most complex challenges with a culture grounded in optimism and rigor. We seek individuals passionate about defining the convergence of quantum and AI and making a meaningful global impact.About the RoleGenerative AI is revolutionizing computational possibilities but reveals the limitations of classical hardware. While diffusion models yield remarkable outcomes, their iterative sampling and high-dimensional score estimation often lead to computational inefficiencies.We are convinced that quantum computing holds the key to overcoming these challenges. As an ML Research Scientist, you will operate at the intersection of generative modeling and quantum acceleration, formulating theoretical foundations and practical applications that merge these domains. Your focus will be on identifying areas where quantum methods can deliver substantial advantages in generative workflows, providing not just incremental enhancements but transformative improvements grounded in mathematical principles.Your ResponsibilitiesGenerative Model Architecture & EfficiencyInnovate state-of-the-art diffusion and score-based generative models.Investigate computational bottlenecks in sampling, denoising, and likelihood estimation.Design and evaluate novel solver techniques for diffusion ODEs/SDEs.Quantum-Classical IntegrationDiscover mathematical structures in generative models that are suitable for quantum acceleration.Prototype hybrid workflows that utilize quantum subroutines to enhance classical processes.Conduct rigorous benchmarks comparing theoretical advantages against practical benefits in realistic scenarios.Research to ProductionTransform research findings into scalable implementations.Collaborate with quantum hardware teams to guide architectural specifications.Facilitate the integration of research insights into production environments.

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.

About World Labs: At World Labs, we create foundational world models capable of perceiving, generating, reasoning, and interacting with the 3D environment. Our mission is to unlock the full potential of AI through spatial intelligence, transforming perception into action, reasoning into insight, and imagination into creation. We believe that spatial intelligence will revolutionize storytelling, creativity, design, simulation, and immersive experiences across both virtual and physical realms. Our world-class team is driven by curiosity and passion, boasting diverse backgrounds in technology, from AI research and systems engineering to product design. This synergy fosters a tight feedback loop between our cutting-edge research and user-empowering products. Role Overview We are seeking an innovative Research Scientist specializing in generative modeling, especially diffusion models, to join our modeling team. This position is ideal for individuals with extensive expertise in applying diffusion models to images, videos, or 3D assets and scenes. While not mandatory, experience in any of the following areas will be considered a significant advantage: Large-scale model trainingResearch in 3D computer vision In this role, you will work closely with researchers, engineers, and product teams to translate advanced 3D modeling and machine learning techniques into practical applications, ensuring our technology stays at the forefront of visual innovation. This position entails substantial hands-on research and engineering work, taking projects from conception to production deployment. Key Responsibilities Design, implement, and train large-scale diffusion models for generating 3D worlds. Develop and experiment with large-scale diffusion models to introduce novel control signals, align with target aesthetic preferences, or optimize for efficient inference. Collaborate closely with research and product teams to comprehend and translate product requirements into actionable technical roadmaps. Contribute actively to all phases of model development, including data curation, experimentation, evaluation, and deployment. Continuously investigate and integrate the latest research in diffusion and generative AI. Serve as a key technical resource within the team, mentoring peers and promoting best practices in generative modeling and machine learning engineering.

At Scale AI, we are at the forefront of artificial intelligence, driving innovation through our advanced data, infrastructure, and tooling that empower the most sophisticated models worldwide. Our teams thrive at the intersection of pioneering research, extensive engineering, and practical deployment, collaborating with leading labs, enterprises, and government entities to explore the vast potential of Generative AI. As AI technology evolves from static models to dynamic, intelligent systems, Scale AI is dedicated to establishing the essential research foundations, evaluation methodologies, and reinforcement learning infrastructure that will shape this transformative era. Join our high-impact research organization, where you will contribute to advancing large language models, post-training evaluation, and agent-based reinforcement learning environments, influencing the future of AI development and implementation. As the Research Scientist Manager, you will spearhead a distinguished team of research scientists and engineers, define the strategic research roadmap, and oversee projects from initial prototyping to final deployment. You will excel in a fast-paced environment, harmonizing deep technical leadership with effective people management, visionary goal setting, and successful delivery.

About Scale AI At Scale AI, we are committed to propelling the advancement of AI technologies. For over eight years, we have been a pioneer in the AI data sector, supporting groundbreaking innovations in areas such as generative AI, defense solutions, and autonomous driving. Following our recent Series F funding round, we are enhancing access to premium data to accelerate the journey towards Artificial General Intelligence (AGI). Building on our legacy of model evaluation for both enterprise and governmental clients, we are expanding our capabilities to establish new benchmarks for evaluations in both public and private domains. About This Role This position is at the leading edge of AI research and practical implementation, concentrating on reasoning within large language models (LLMs). The successful candidate will investigate critical data types vital for evolving LLM-based agents, including browser and software engineering agents. You will significantly influence Scale’s data strategy by pinpointing optimal data sources and methodologies to enhance LLM reasoning. To excel in this role, you will require a profound understanding of LLMs, planning algorithms, and fresh approaches to agentic reasoning, alongside inventive solutions to challenges in data generation, model interaction, and evaluation. Your contributions will lead to transformative research on language model reasoning, facilitate collaboration with external researchers, and engage closely with engineering teams to translate cutting-edge advancements into scalable, real-world applications.

At Causal Labs, we are on a groundbreaking mission to develop general causal intelligence—artificial intelligence that not only predicts future events but also determines the most effective actions to influence those outcomes.To achieve this monumental goal, we are constructing a Large Physics Foundation Model (LPM). Our focus is on domains governed by physical laws, which inherently exhibit cause-and-effect relationships, setting them apart from traditional visual or textual data.Weather serves as the ideal training environment for our LPM, being one of the most extensively observed physical systems available. It provides immediate, objective feedback from sensory observations and boasts data scales significantly larger than those currently employed to train existing language models.Our team at Causal Labs includes leading researchers and engineers with backgrounds in self-driving technology, drug discovery, and robotics, hailing from prestigious organizations such as Google DeepMind, Cruise, Waymo, Meta, Nabla Bio, and Apple. We firmly believe that achieving general causal intelligence will represent one of the most critical technological advancements for our civilization.We are seeking innovative researchers eager to confront unsolved challenges in the field.This role presents an opportunity to create powerful models rooted in observable feedback and verifiable ground truths. If you possess experience in pioneering research and training large-scale models from the ground up in areas such as language and vision models, robotics, or biology, we invite you to join our mission.

Join Handshake as a Machine Learning Research Scientist and contribute to groundbreaking projects that leverage advanced algorithms and data analysis to drive innovation. In this role, you will collaborate with a dynamic team to design, implement, and evaluate machine learning models that enhance our products and services. Your expertise will be pivotal in unlocking new insights from data, improving user experiences, and shaping the future of our technology.

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.

At Scale AI, we collaborate with leading AI laboratories to supply high-quality data and foster advancements in Generative AI research. We seek innovative Research Scientists and Research Engineers with a strong focus on post-training techniques for Large Language Models (LLMs), including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and reward modeling. This position emphasizes optimizing data curation and evaluation processes to boost LLM performance across text and multimodal formats. In this pivotal role, you will pioneer new methods to enhance the alignment and generalization of extensive generative models. You will work closely with fellow researchers and engineers to establish best practices in data-driven AI development. Additionally, you will collaborate with top foundation model labs, providing critical technical and strategic insights for the evolution of next-generation generative AI models.

Join us at Physical Intelligence as a Research Scientist, where you will be at the forefront of innovation in machine learning and robotics. We are in search of exceptional researchers across all experience levels who demonstrate a strong track record of impactful research results. Ideal candidates will possess a solid foundation in both practical implementation and theoretical frameworks, showcasing a blend of system-building capabilities and significant conceptual, algorithmic, or theoretical advancements. We value diverse backgrounds and encourage applications from both traditional academic researchers and those with unique, unconventional experiences.We are committed to fostering a diverse and inclusive workplace. In accordance with the San Francisco Fair Chance Ordinance, we welcome applications from qualified individuals with arrest and conviction records.

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.

Join Anthropic as a Senior Research Scientist on our Reward Models team, where you will spearhead groundbreaking research aimed at enhancing our understanding of human preferences at scale. Your innovative contributions will directly influence how our AI models, including Claude, align with human values and optimize for user needs. You will delve into the forefront of reward modeling for large language models, designing novel architectures and training methodologies for Reinforcement Learning from Human Feedback (RLHF). Your research will explore advanced evaluation techniques, including rubric-based grading, and tackle challenges such as reward hacking. Collaboration is key, as you'll work alongside teams in Finetuning, Alignment Science, and our broader research organization to ensure your findings result in tangible advancements in AI capabilities and safety. This role offers you an opportunity to address critical AI alignment challenges, leveraging cutting-edge models and substantial computational resources to further the science of safe and capable AI systems.

Become a Pioneer in Sleep FitnessAt Eight Sleep, we're dedicated to unlocking human potential through optimal sleep. As the world's first sleep fitness company, we are revolutionizing what it means to be well-rested by creating the most advanced hardware, software, and AI technology. Our innovative products enhance mental, physical, and emotional performance by transforming each night into a personalized, data-driven recovery journey. Trusted by high achievers, professional athletes, and health-conscious individuals across over 30 countries, we have been recognized by Fast Company as one of the Most Innovative Companies in 2019, 2022, and 2023, and honored twice by TIME's “Best Inventions of the Year.” Our high-performance team operates with speed, focus, and a commitment to impact. We don't just create; we refine and obsess over every detail to ensure our members sleep better and wake up stronger.Every position at Eight Sleep offers the opportunity to innovate cutting-edge technology, collaborate with exceptional talent, and contribute to a future where sleep is a powerful tool for well-being. If you're ready to break away from the ordinary and eager to build at the forefront of possibility, this is your chance to join us in reshaping how the world sleeps and what we can achieve upon waking.Our Culture: High Standards, No CompromiseOur mission demands intensity, and at Eight Sleep, we embody the mindset of the world's top performers: focused, relentless, and committed to being in the top 1% of our field. Inspired by the relentless drive of legends like Kobe Bryant, we apply that mentality to bold ideas, next-gen technology, and impeccable execution. This is not a standard 9-to-5 role; our team is dedicated, often working 60+ hours per week—not out of obligation, but out of passion. If you thrive under pressure and seek to do the most meaningful work of your career, you'll find a home here. If you prefer an easier path, this position is not for you.Your RoleAs a Machine Learning Research Scientist at Eight Sleep, you will be at the cutting edge of sleep innovation. Your mission will be to leverage innovative technology, minimalistic design, and proven clinical science to personalize and enhance sleep experiences, fundamentally changing how people sleep for the better.Our revolutionary temperature-regulated technology, the Pod, has been recognized as a game changer, enhancing health and happiness by transforming sleep. Join us in making sleep count for more.

About Scale AI At Scale AI, we are dedicated to propelling the advancement of AI applications. Over the past eight years, we have established ourselves as the premier AI data foundry, supporting groundbreaking innovations in fields such as generative AI, defense technologies, and autonomous vehicles. Following our recent Series F funding round, we are intensifying our efforts to harness frontier data, paving the way toward achieving Artificial General Intelligence (AGI). Our work with enterprise clients and governments has enhanced our model evaluation capabilities, allowing us to expand our offerings for both public and private evaluations. About the ACE Team The Agent Capabilities & Environments (ACE) team, a vital part of Scale’s Research organization, unites customer-focused Researchers and Applied AI Engineers. Our primary mission is to conduct research on agent environments and reinforcement learning reward signals, benchmark autonomous agent performance in real-world contexts, and develop robust data programs aimed at enhancing the capabilities of Large Language Models (LLMs). We are committed to creating foundational tools and frameworks for evaluating models as agents, focusing on autonomous agents that interact dynamically with a wide range of external environments, including code repositories and GUI interfaces. About This Role This position sits at the cutting edge of AI research and its practical applications, concentrating on the data types necessary for the development of state-of-the-art agents, including browser and software engineering agents. The ideal candidate will investigate the data landscape required to propel intelligent and adaptable AI agents, steering the data strategy at Scale to foster innovation. This role demands not only expertise in LLM agents and planning algorithms but also creative problem-solving skills to tackle novel challenges pertaining to data, interaction, and evaluation. You will contribute to influential research publications on agents, collaborate with customer researchers, and partner with the engineering team to transform these advancements into scalable real-world solutions.

At Merge Labs, we are at the forefront of research, dedicated to uniting biological and artificial intelligence to enhance human capability, autonomy, and overall experience. Our innovative approach focuses on developing revolutionary brain-computer interfaces that offer high-bandwidth interaction with the brain, seamlessly integrate advanced AI, and are designed to be safe and accessible for everyone.About the Team:Our Bio team is responsible for designing, constructing, and characterizing the biotechnologies that underpin the next generation of brain-computer interfaces. By integrating molecular engineering, synthetic biology, neuroscience, and cutting-edge physical methods such as ultrasound, we aim to establish less invasive, high-bandwidth connections with neurons. The Bio team is dedicated to developing our core molecular technologies, validating their performance both in vitro and in vivo, and showcasing their advanced capabilities in animal models. We create custom experimental setups and pipelines while collaborating closely with engineers and data scientists to tackle some of the most challenging problems in biotechnology.About the Role:We are seeking a Senior/Principal Machine Learning Biophysicist to spearhead the creation of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks. You will build the molecular modeling foundations of the company from first principles, establishing tools and workflows for simulating, analyzing, and interpreting biomolecular dynamics to elucidate function relationships. Over time, your contributions will help translate these frameworks into predictive models that expedite molecular engineering, guide experimental campaigns, and facilitate the discovery of highly functional molecules.Key Responsibilities:Develop the scientific and engineering framework for protein structure modeling and molecular dynamics, along with integrations into downstream ML frameworks.Collaborate with wet-lab scientists to establish realistic optimization objectives and encode domain-specific priors and constraints.Prototype modeling frameworks utilizing internal and public datasets; benchmark and validate performance.Make complex analyses accessible to non-domain experts through democratization of first-principles analysis.Lead the development of ML frameworks that explicitly incorporate first-principles priors.Stay abreast of the latest advancements in deep learning and molecular dynamics.

About Wispr FlowAt Wispr Flow, we strive to make device interaction as seamless as conversing with a friend.Wispr Flow has revolutionized voice dictation, now preferred by users over traditional keyboards due to its unparalleled accuracy on the first attempt. Our platform is context-aware, personalized, and effective across all devices, whether desktop or mobile.By 2026, we aim to expand beyond dictation to develop native actions within an agentic framework that comprehends and responds to user needs reliably.Our diverse team comprises AI researchers, designers, growth specialists, and engineers dedicated to reimagining human-computer interaction. We value team members who prioritize open communication, exhibit a user-centric mindset, and pay meticulous attention to detail. Our collaborative environment fosters spirited discussions, truth-seeking, and tangible impact.Having achieved a remarkable 150% revenue growth quarterly for the past year, we have successfully raised $81 million from top-tier venture capitalists and renowned angel investors.

Position OverviewAt Sentra, we are pioneering the development of organizational superintelligence through innovative memory infrastructure that intelligently processes time, causality, and context. As a Machine Learning Research Scientist, you will address fundamental challenges in knowledge representation, temporal reasoning, and semantic compression. Your mission will be to design and implement sophisticated systems that preserve the execution state for entire organizations, transforming millions of micro-events into robust knowledge and identifying patterns for predicting future events.Key ResponsibilitiesDevelop LLM-powered information extraction pipelines to convert unstructured communications and textual data into structured entity-relationship models.Create memory consolidation algorithms that validate information through multiple observations, merge duplicate entities, and efficiently prune transient data.Architect temporal knowledge graphs that represent organizational execution states as dynamic, continuously updated frameworks instead of static records.Implement graph attention mechanisms and reasoning systems for intricate causal queries regarding blockers, dependencies, and outcome patterns.Conduct research on lossy semantic compression using information-theoretic principles to distill event streams into query-relevant long-term memory.Design entity resolution systems that effectively manage identity evolution, where entities may merge, split, and transform over time.Construct meta-learning systems that uncover organizational patterns and discern when current situations align with historical indicators of success or failure.Innovate privacy-preserving cross-organizational learning approaches utilizing federated learning and differential privacy techniques.Publish research findings and actively contribute to the wider research community focused on knowledge graphs and organizational intelligence.

At Scale AI, we are the premier partner for data and evaluation in the rapidly evolving field of artificial intelligence. Our commitment to advancing the assessment and benchmarking of large language models (LLMs) positions us at the forefront of AI innovation. We are dedicated to creating leading-edge LLM evaluation methodologies that set new benchmarks for model performance. Our research teams collaborate with the top AI laboratories in the industry to provide high-quality data, accelerate progress in generative AI research, and inform what excellence looks like in this domain. As a Staff Machine Learning Research Scientist on our LLM Evals team, you will spearhead the creation of novel evaluation methodologies, metrics, and benchmarks to assess the strengths and weaknesses of cutting-edge LLMs. Your work will shape our internal strategies and influence the broader AI research community, making this role essential for establishing best practices in data-driven AI development.

About UsAt Preference Model, we are pioneering the development of next-generation training data that drives the future of artificial intelligence. While current models demonstrate significant capabilities, they often fall short in diverse applications due to many tasks being out of distribution. We create reinforcement learning environments where models tackle real-world research and engineering challenges, continuously iterating and learning from authentic feedback loops.Our founding team, with experience from Anthropic’s data division, has built the infrastructure and datasets that support the Claude AI. We collaborate with top-tier AI laboratories to accelerate AI's journey toward its transformative potential, and we are proudly backed by a16z.About the RoleWe envision a future where models can autonomously train on their weaknesses. We seek innovative thinkers eager to explore the limits of self-directed learning. In this position, you will meld research with engineering, implementing cutting-edge methodologies and influencing research trajectories.Representative Projects:Architect and enhance our core reinforcement learning infrastructure, developing clean training abstractions and distributed experiment management systems to accommodate increasingly complex research workflows.Design, implement, and validate training environments, evaluations, and methodologies for reinforcement learning agents.Enhance performance through profiling, optimization, and benchmarking. Implement efficient caching techniques and debug distributed systems to expedite training and evaluation processes.Collaborate with cross-functional teams in research and engineering to establish automated testing frameworks, design user-friendly APIs, and build scalable infrastructure that propels AI research forward.You May Be a Good Fit If You:Are proficient in Python and familiar with frameworks like PyTorch or Jax.Have hands-on experience in training and conducting machine learning research on large language models.Can effectively balance research exploration with practical engineering implementation.Enjoy pair programming and prioritize code quality, testing, and performance.Possess strong systems design and communication skills.Have a solid understanding of reinforcement learning algorithms and stay updated with current publications in the field.

Zyphra is a cutting-edge artificial intelligence firm headquartered in the vibrant city of San Francisco, California.Position Overview:As a Research Scientist specializing in Model Architectures, you will play a pivotal role in Zyphra’s AI Architecture Research Team. Your responsibilities will include the design and thorough evaluation of innovative model architectures and training methodologies aimed at enhancing essential modeling capabilities (e.g., loss per flop or loss per parameter) and tackling core limitations inherent in current models. You will collaborate closely with our pre-training team to ensure that your findings are seamlessly integrated into our next-generation models.Qualifications:A strong research acumen and intuition.Proven ability to navigate research projects from initial conception to execution and final write-up.Exceptional implementation and prototyping skills, with the capability to swiftly transform ideas into experimental outcomes.A collaborative spirit and the ability to thrive in a fast-paced research environment.A deep curiosity and enthusiasm for understanding intelligence.Requirements:Experience with long-term memory, RAG/retrieval systems, dynamic/adaptive computation, and alternative credit assignment strategies.Knowledge of reinforcement learning, control theory, and signal processing techniques.A passion for exploring and critically evaluating unconventional ideas, with the ability to maintain a unique perspective.Familiarity with modern training pipelines and the hardware necessities for designing efficient architectures compatible with GPU hardware.Strong understanding of experimental methodologies for conducting rigorous ablations and hypothesis testing.High proficiency in PyTorch and Python programming.Ability to quickly assimilate into large pre-existing codebases and contribute effectively.Prior publication of machine learning research in reputable venues.Postgraduate degree in a scientific discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Physics).Why Join Zyphra?We emphasize a structured research methodology that systematically addresses ambitious challenges in AI.