Machine Learning Research Specialist - RF Foundation Models

Role overview Distributed Spectrum is seeking a Machine Learning Research Specialist to advance research on RF Foundation Models. The focus of this position is on developing new machine learning methods that support radio frequency communications. What you will do Lead research into machine learning techniques tailored for RF communications. Create and evaluate new data-driven models to enhance connectivity. Work closely with colleagues who combine expertise in machine learning and radio frequency technology. The team Distributed Spectrum’s team investigates innovative approaches to connectivity and communication. Their projects push the boundaries of what is possible in machine learning and RF signal processing. Location This position is located in New York City.

About UsMirror Physics is an innovative AI company based in New York City, pioneering the next generation of scientific simulation technologies. Our mission is to create intelligent systems that grasp the fundamental principles of physics, thereby providing essential acceleration for advanced research and development across various technological fields. We are currently developing a leading-edge AI platform that predicts experimental outcomes in chemistry and materials science, seamlessly integrating physical simulation with high-throughput experimental verification. This endeavor aims to hasten discoveries in biotechnology, energy, manufacturing, and more. Supported by top-tier investors and scientific experts, we are seeking to expand our research team during this crucial period in the industry.The RoleAs the principal AI researcher focusing on physics model development, you will lead efforts in designing innovative architectures, training algorithms, and evaluation processes to transform vast amounts of physical simulation data into scalable, precise, and versatile predictive engines applicable in both scientific and industrial contexts.Key ResponsibilitiesCreate robust, scalable, and universally applicable atomistic models with high fidelity across various chemical domains.Compile diverse and multi-fidelity datasets into cohesive training corpora; innovate new objectives to enhance data efficiency.Produce groundbreaking datasets that encompass an unmatched variety of chemical systems, consistently computed at the highest theoretical levels suitable for general chemistries.Design diagnostic tools for model performance evaluation, failure mode assessment, and uncertainty quantification; propose new benchmarks to rigorously test predictive accuracy, physical consistency, and extrapolation capabilities.Develop downstream tools to improve model precision and processing speed, including model distillation and fine-tuning techniques.Collaborate with the AI-for-science community through research publications and contributions at leading conferences such as NeurIPS, ICML, and ICLR.Mentor junior researchers and work closely with applied science and engineering teams.

OverviewAt OXMAN, a pioneering nature-focused research and design firm based in Manhattan, we leverage nature-centric solutions to tackle pressing challenges at the intersection of Nature and Humanity. Our interdisciplinary approach spans various sectors, fostering innovative concepts that redefine the interaction between the built environment and the natural ecosystem.Our initiative, EDEN, is dedicated to applied ecological systems operating on architectural, urban, and landscape scales. We are at the forefront of creating a digital design framework that engineers biodiverse and resilient ecosystems by developing software that empowers designers and engineers to incorporate essential ecosystem services—such as carbon sequestration, thermal buffering, and biodiversity—into their master planning and design processes. Our mission is to elevate ecosystem services from undervalued byproducts to vital infrastructure elements. We invite talented professionals to join us in revolutionizing the future of nature-centric design.We are currently seeking a Machine Learning Research Engineer to integrate into our dynamic design team in New York City. This role will be pivotal in supporting our efforts to deliver exceptional, data-driven design proposals utilizing EDEN’s innovative suite of ecosystem engineering tools.

About General IntuitionGeneral Intuition is at the cutting edge of AI research, dedicated to developing foundational models that excel in deep spatial and temporal reasoning. Over the past year, we have advanced the capabilities of AI agents that navigate complex environments, created world models that serve as training grounds for these agents, and innovated video understanding models aimed at real-world application.We are proud to have raised a seed funding round of $133 million from General Catalyst and Khosla, driving our mission to uncover the next generation of intelligence.What We're Looking ForMinimum of 5 years of experience in deep learning research or reinforcement learning, specifically with embodied agents or simulation environments.Solid foundation in representation learning and generative modeling, particularly using architectures like diffusion models, VAEs, and transformers applied to video data.Experience with world models and predictive control — you possess knowledge on training models that simulate dynamics and plan actions within learned environments.Proficiency in reinforcement learning (RL, model-based RL, or imitation learning), coupled with the capability to design and evaluate policy networks.Excellent programming skills in Python and deep learning frameworks such as PyTorch.Strong experimental capabilities — adept in handling large-scale training, evaluation pipelines, and managing intricate datasets or simulations.Publications or contributions to open-source projects in domains such as world modeling, simulation learning, or agent policies are a significant advantage.In-person requirement: We are specifically seeking candidates located in New York City, with a commitment to working in the office five days a week.Ownership & scientific rigor: You are committed to seeing ideas through from conception to proof of concept to deployment. You write clean, reproducible code and uphold high standards for experimental validity.Performance and scaling mindset: You understand how research can be translated into production systems, with a keen awareness of compute efficiency, distributed training, and potential data bottlenecks.Curiosity-driven and results-oriented: You thrive on tackling open-ended problems, while also knowing how to set measurable goals and deliver impactful systems.Passion for gaming & simulation: A strong interest in interactive environments and physics-based simulations is highly desirable.

About Mirage Mirage builds an AI-powered video platform that connects production and editing through natural language processing. Our models use contextual awareness to mirror the choices of skilled editors, streamlining workflows for experienced teams and making video creation more accessible to a wider audience. Learn More Our Product (Captions by Mirage) Our Research (Seeing Voices, technical white paper) Latest Updates (Mirage on X / Twitter) Mirage has been featured in TechCrunch, Forbes AI 50, and Fast Company. Our Investors Mirage is backed by leading venture firms and entrepreneurs, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, and others. Location Requirement All roles at Mirage require in-person work at our Union Square headquarters in New York City. Role Overview: Research Engineer – Large Language Models Mirage seeks a Research Engineer to design, build, and scale systems for training and deploying large language models, with a focus on multimodal creative applications in video analysis. This role works closely with researchers to turn new ideas into efficient, production-ready systems that strengthen our platform.

Anthropic is looking for a Research Engineer with a focus on Machine Learning, particularly Reinforcement Learning (RL) Velocity. This position involves collaborating with a team to design, build, and refine machine learning systems. Much of the work centers on experimenting with new ideas and advancing AI research. What you will do Work alongside researchers and engineers to develop and optimize machine learning models Explore new methods in reinforcement learning to accelerate progress Contribute to projects that push the boundaries of AI capabilities Location and travel This role offers flexibility to work remotely, with some required travel. Anthropic maintains offices in San Francisco, CA and New York City, NY.

Join Our Innovative TeamBecome a pivotal member of our team dedicated to creating the world's first biological reasoning model. Collaborate with us on groundbreaking generative foundational models that interpret biological systems across various scales—from molecular interactions to entire organisms—empowering us to predict, comprehend, and manipulate living systems like never before.Output is currently in stealth mode, driven by a team of seasoned founders and biotech experts who have successfully exited ventures in AI and biotechnology, supported by leading venture capital firms including Y Combinator.As a Machine Learning Engineer, you will partner with our founders and talented team members to innovate and deploy advanced AI solutions that facilitate intricate biological reasoning across diverse scales.Develop foundational models for biological systems capable of encoding and decoding biological data at scale.Create deep generative models tailored for biological applications, experimenting with innovative architectures to capture the complexities of multi-scale biological systems.Work on distributed training systems to scale our models to billions of parameters, focusing on performance and efficiency across multi-GPU and multi-node configurations while processing extensive biological datasets.Design efficient data pipelines to manage and process large biological datasets, tackling challenges in data loading, partitioning, and memory optimization.Establish and implement robust evaluation frameworks for complex biological models, ensuring data integrity and preventing leakage across dataset splits.

Anthropic is looking for a Research Engineer focused on model evaluations. This position involves research and development to assess and strengthen the performance of AI models. Teams are based in San Francisco and New York City, and the role supports remote work with required travel. Key responsibilities Design and implement evaluations for Anthropic's AI models Collaborate with team members to enhance model performance Contribute to research that pushes the boundaries of AI systems Location Remote-friendly (travel required) San Francisco, CA New York City, NY

About UsAt Percepta, we are on a mission to revolutionize pivotal industries through applied AI. Our focus is on ensuring that the sectors that drive our world, including healthcare, manufacturing, and energy, harness the power of cutting-edge technology. To achieve this, we closely collaborate with top-tier clients to facilitate AI transformation. We unite:Expertise in engineering, product, and research that is deployed at the forefront of innovation.Mosaic, our proprietary toolkit designed for the rapid implementation of intelligent workflows.Strategic alliances with leading firms such as Anthropic, McKinsey, AWS, and others within the General Catalyst portfolio.Our team consists of a dynamic group of Applied AI Engineers, Embedded Product Managers, and Researchers who are passionate about integrating AI to enhance everyday experiences. Percepta is a proud partner of General Catalyst, a global leader in transformation and investment.Role OverviewAs a Research Engineer/Scientist specializing in LLM Modeling at Percepta, you will be at the forefront of developing and deploying large-scale language models. You will engage in pre-training, post-training (including instruction tuning, alignment, and distillation), reinforcement learning, and the crafting of specialized architectures to enhance reasoning, decision-making, and adaptability across critical sectors.Collaboration is key as you will work closely with Embedded Product Managers and engineers to create innovative yet practical decision systems that significantly transform business operations.Identify significant challenges and formulate research strategies encompassing pre-training, post-training, RL, and specialized model development.Prototype and scale training pipelines for large language models, experimenting with architectures, optimization methods, and post-training tactics.Contribute to the infrastructure necessary for high-performance distributed training.Conduct extensive real-world evaluations that yield millions in value.Collaborate with applied AI engineers to transition successful research findings into actionable features within our Mosaic platform.Effectively communicate research outcomes to both technical and non-technical stakeholders, ensuring clarity on the implications of research and its applications.

Role Overview root-access is hiring a Machine Learning Engineer in New York City. This role works closely with the tech team to design and build machine learning algorithms that support and improve our products. The position involves hands-on development, data analysis, and delivering solutions that scale as our business grows. What You Will Do Develop machine learning models tailored to product needs Analyze complex data sets to extract insights and guide model improvements Create solutions that can scale to meet increasing demand Collaborate with engineers and product teams to integrate AI-driven features Who We’re Looking For Experience building and deploying machine learning algorithms Strong interest in artificial intelligence and its practical applications Ability to work with large, complex data sets Comfort working in a collaborative, evolving tech environment

Who We AreAt Percepta, our mission is to revolutionize essential sectors through applied AI. We are dedicated to ensuring that key industries, such as healthcare, manufacturing, and energy, leverage cutting-edge technology. By collaborating with industry leaders, we facilitate AI transformation.Expertise in engineering, product development, and research.Mosaic, our proprietary toolkit for swift deployment of agentic workflows.Strategic alliances with companies such as Anthropic, McKinsey, AWS, and others in the General Catalyst portfolio.Our team is rapidly expanding, consisting of Applied AI Engineers, Embedded Product Managers, and Researchers who are passionate about integrating AI advancements into our daily lives. Percepta is a direct partnership with General Catalyst, a global leader in transformation and investment.About the RoleAs a Research Engineer/Scientist (Optimization) at Percepta, you will play a crucial role at the convergence of AI research and impactful real-world applications. You will push the boundaries of decision-making in vital industries by integrating state-of-the-art machine learning with robust optimization research. Collaborate closely with our Embedded Product Managers (EPMs) and engineers to ensure that our advanced decision systems are both innovative and practically beneficial for transforming organizational operations.ResponsibilitiesEstablish and lead ambitious research initiatives that broaden the horizons of data-driven decision-making.Develop innovative optimization and machine learning methodologies for significant challenges such as planning, scheduling, routing, pricing, and inventory management.Create high-fidelity simulators and comprehensive benchmarks that accurately reflect real-world constraints, uncertainties, and multi-objective trade-offs.Translate research into practical applications by partnering with engineers to swiftly prototype solutions and effectively implement research insights.You May Be a Great Fit If YouHold a degree in Computer Science, Operations Research, Industrial Engineering, or Applied Mathematics (MS/PhD preferred) or possess equivalent research or industry experience.Have demonstrated experience in optimization and machine learning.Exhibit a strong ability to collaborate and communicate effectively across multidisciplinary teams.

Join Our Team as a Machine Learning Engineer! At FanDuel, we recognize that data is the lifeblood of our organization. As a Machine Learning Engineer, you will harness the power of our extensive real-time and relational datasets to drive insights that enhance user experiences. Your analytical skills will transform every interaction—from user clicks to game outcomes—into actionable intelligence, enabling us to make faster, more informed decisions that keep us ahead of the competition. Your role will be pivotal in fostering collaboration across teams. You will serve as the bridge between engineering teams, facilitating the development of our online applications while ensuring that essential data is available for feature engineering. Collaborating with Data Scientists and Analysts, you will help bring AI-driven insights into production and validate their effectiveness. Your efforts will be instrumental in organizing, modeling, and presenting data in a manner that provides a unified information structure, empowering FanDuel to respond intelligently to market dynamics and on-field events. If you are a Machine Learning Engineer with aspirations to thrive in a big data environment, we encourage you to apply! Your Responsibilities: Design and implement intelligent search systems incorporating typeahead search, vector search, and ML personalization to optimize user experience. Contribute to the creation of scalable serving systems for ML and GenAI/LLM models. Develop platform features and tools (e.g., CLI, SDK, Infrastructure Automation) that enhance the ML model lifecycle and GenAI/LLM application deployment. Utilize business intelligence tools such as Tableau, Knime, and Looker. Ensure data security and privacy compliance (e.g., GDPR, CPP). Implement data governance and testing frameworks. Support continuous integration and delivery of data products. Participate in an inclusive culture that prioritizes excellence, personal growth, and well-being.

At Garner Health, we are dedicated to revolutionizing the healthcare economy by making high-quality and affordable care accessible to all.We are fundamentally reimagining the healthcare landscape in the U.S. by collaborating with employers to redesign healthcare benefits using transparent incentives and powerful, data-driven insights. Our innovative approach directs employees toward higher-quality, lower-cost care, fostering a healthcare system that benefits everyone. This means improved health outcomes for patients, more effective spending of healthcare dollars for employers, and rewarding physicians for providing exceptional care rather than performing unnecessary procedures.Garner is recognized as one of the fastest-growing healthcare technology companies nationally. Our products are trusted by the most sophisticated employers and providers in the industry, and we are building a team of talented, mission-driven individuals who are eager to make a significant impact on healthcare at scale.About the Role:We are on the lookout for an outstanding Staff Machine Learning Engineer to join our dynamic team. At Garner, our Engineering team addresses complex technical challenges that have a meaningful impact on our mission. Our systems analyze billions of medical records to evaluate healthcare providers nationwide. We operate on AWS with Kubernetes and consistently integrate new technologies as our system load grows exponentially each year. We encourage our engineers to advocate for cutting-edge industry practices as we wholeheartedly incorporate AI into every aspect of our operations and standards. We value teammates who take pride in delivering exceptional work and nurturing a robust team culture.Where You Will Work:This position is based in our New York City office located in the Financial District. Candidates must be available to work in the office three days a week: Tuesday, Wednesday, and Thursday.What You Will Do:Lead technical strategy and execution at the team level over multi-quarter time frames.Utilize AI to build, optimize, and maintain machine learning, data, and software systems.Elevate the technical capabilities of engineers throughout the organization.Collaborate across disciplines to deepen domain understanding and iterate on projects.Ensure user privacy and security through adherence to best practices.

About Mirage Mirage builds an AI-powered video platform that brings production and editing together through natural language. The team focuses on models with strong contextual understanding, aiming to match the creative decisions of skilled editors. This approach boosts productivity for experienced teams and makes advanced video creation accessible to a wider audience. Learn More Product: Captions by Mirage Research: Seeing Voices (technical white paper) Updates: Mirage on X / Twitter Press: TechCrunch, Forbes AI 50, Fast Company Investors Mirage is backed by leading investors and entrepreneurs, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, and General Catalyst. Role Overview: Research Scientist, Video Generation This position is based at Mirage’s headquarters in Union Square, New York City. In-person attendance is required. Mirage seeks a Research Scientist with a focus on video generation. As an early member of the team, this person will help address foundational challenges in generative media. The role involves developing new modeling techniques, designing training methods, and scaling large video models for real-world use. Work in this area shapes systems used by millions of creators. What You Will Do Advance the quality, controllability, and realism of video generation models Focus on areas such as facial expression synthesis and scene understanding Develop and refine modeling and training strategies for large-scale video systems Collaborate with an interdisciplinary team to solve technical and creative challenges in generative media

Mirage builds an AI-native platform for video production and editing, centered in Union Square, New York City. The platform uses natural language to guide intelligent orchestration, allowing advanced models to understand context and mimic the creative decisions of experienced editors. This approach aims to boost productivity for professional teams and open up video creation to a wider audience. About the Team The team at Mirage brings together people from a range of backgrounds, blending technical and artistic skills to solve tough challenges in generative media. The work goes beyond routine model development, focusing on problems that remain unsolved across the industry. Role Overview: Research Scientist, Large Language Models This early team role offers the chance to shape the core technology behind Mirage. The position involves tackling foundational questions in generative AI and creative tooling, with the potential to influence how people create and edit video for years to come.

About TennrAt Tennr, we are transforming the way healthcare referrals are managed. When patients are referred to specialists, they often face frustrating delays. Our innovative platform, powered by RaeLM™, ensures that every referral is processed swiftly and accurately, allowing healthcare providers to capture more referrals and reduce denials. Join us in revolutionizing the healthcare experience.About the RoleAs the pioneering ML Ops Engineer at Tennr, you will be instrumental in developing and refining our Machine Learning and AI infrastructure. You will be responsible for creating and optimizing machine learning training and inference pipelines that can manage escalating traffic demands and an expanding range of products. Your expertise will ensure that our AI-driven healthcare platform operates on robust, scalable, and efficiently deployed models.Our Machine Learning team is at the forefront of innovation, developing proprietary VLMs, LLMs, and other specialized models tailored to address critical healthcare challenges. This is not just a position of maintaining existing systems; it is an opportunity to lead groundbreaking experimentation and the introduction of new capabilities. Your contributions will significantly impact our ML and data systems, enhancing Tennr's ability to solve vital issues for patients and healthcare providers.Key ResponsibilitiesDesign, architect, and implement scalable ML software systems for model deployment and management.Maintain infrastructure that supports efficient ML operations, including data pipelines, model evaluations, and large-scale training.Work collaboratively with ML engineers, software developers, and cross-functional teams to integrate models seamlessly with data pipelines and products.Identify and resolve production issues while continuously enhancing system performance and efficiency.Develop tools for online and offline evaluation of ML and LLM systems.

About the Role qloo is looking for a Machine Learning Engineer focused on Large Language Models (LLMs) and personalization. This position is based in New York City. What You Will Do Design and build machine learning models to improve user experience and increase engagement. Work closely with teams across the company to implement advanced algorithms. Support data-driven decisions by contributing expertise in LLMs and personalization techniques. Collaboration This role involves frequent collaboration with cross-functional groups, including product and data teams.

About the Role Waymo is looking for a Senior Machine Learning Engineer focused on Prediction & Planning to join the System Architecture team. This position is based in Mountain View, CA, San Francisco, CA, or New York City, NY. What You Will Do Design and implement advanced machine learning algorithms for autonomous vehicle prediction and planning. Develop systems that improve the safety and efficiency of self-driving technology. Collaborate with engineers and researchers across teams to advance core capabilities in autonomous driving. Impact Your work will help shape the systems that guide Waymo’s vehicles, supporting safer and more reliable autonomous transportation.

Hello! I'm Brian, Co-Founder of Egra. We are thrilled to announce that we have successfully raised $5.5 million to develop foundational models for interpreting brain signals, and we are eager to welcome our inaugural research scientist to our innovative team.In this role, you will be empowered to take complete ownership of your projects from the outset. Say goodbye to prolonged onboarding processes, the need for excessive approvals, and bureaucratic hurdles. You will collaborate directly with the founders, tackling complex technical challenges and utilizing the resources available to drive solutions. You will have the unique opportunity to shape the research vision, make key architectural choices, and lay the groundwork for our core technology. If you're someone who thrives in an environment that offers high autonomy and desires to make a significant impact on the direction of a pioneering company, this role is perfect for you.Key ResponsibilitiesEEG (electroencephalography) is one of the most challenging real-world signal modalities in machine learning due to its low signal-to-noise ratio, vast subject variability, and inconsistencies across devices. Many shy away from this domain for these reasons.As our founding research scientist, you will collaborate closely with us to discover effective methodologies. Here’s a glimpse into the projects you will spearhead:Developing self-supervised pretraining objectives that enhance generalization across various subjects, devices, and recording conditions.Critically evaluating existing methods (including recent EEG foundation model studies) to identify their limitations across datasets and conditions, particularly under distribution shifts.Creating robust evaluation protocols that effectively differentiate genuine progress from noise, ensuring we maintain rigorous benchmarks.Documenting internal research findings through comprehensive memos that contribute to the lab's collective knowledge — addressing questions like "why model X fails on dataset Y" and "what Z dataset misleads us about".This position is about cultivating a thorough understanding of a modality that remains largely unexplored with modern machine learning approaches and transforming this insight into impactful advancements.Vision for the FutureWe are striving towards a future where thought serves as a direct interface.Imagine composing a message silently as it types itself or navigating an AR environment without any physical interaction. Software that adapts to your cognitive state in real-time. We are developing a universal interface connecting human cognition with digital actions.Our product development encompasses three pivotal layers:A Neural Encoder: a foundational model designed to translate raw EEG data into resilient, reusable embeddings that function seamlessly across diverse devices and recording environments.

Join Our Team as a Machine Learning Infrastructure EngineerAt Fauna Robotics, we are on the forefront of revolutionizing robotics through cutting-edge machine learning technologies. As a Machine Learning Infrastructure Engineer, you will play a pivotal role in constructing and enhancing the machine learning systems that drive our innovative robotic solutions. Your expertise will be essential in designing, managing, and optimizing the infrastructure required for training, evaluating, and deploying ML models that facilitate robot locomotion, perception, manipulation, navigation, and interaction with humans.This position is an exciting opportunity to work at the convergence of machine learning and systems engineering, ensuring that our ML training and deployment systems are resilient, efficient, and scalable as we transition from prototypes to fully functional production systems.