Наука и R&D вакансии - гибрид

Location: New York, NY (HQ); San Francisco, CA Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT RAMP LABS Ramp Labs is Ramp's AI research group. We post-train models on finance work, build the environments and evals they learn from, and ship them into the products 70,000+ companies use to move money. We think the defensible part of an AI product is the loop: proprietary environments, evals that track what customers care about, and models trained against both. ABOUT THE ROLE Every model decision at Ramp Labs runs through a benchmark. Which model to route to. Whether a post-training run worked. Whether an agent is ready to post a corrective journal entry to a customer's ledger without a human in the loop. You'll own Ramp's benchmarking effort. You'll turn real finance work (expense policy review, invoice coding, month-end close, spreadsheet Q&A) into benchmarks and RL environments, build the graders, and tell the team what is actually improving. WHAT YOU’LL DO - Build benchmarks from real workflows: turn agent traces, customer edge cases and expert labels into versioned benchmark suites for long-horizon agents that run for hours across tools and systems, plus tool use, multi-step reasoning and financial judgment - Own the benchmarking effort: decide what we measure, run benchmark suites across models and agent versions, and report results the team makes decisions on - Design graders: rubrics, programmatic checks and llm-as-judge, calibrated against human agreement, with a clear view of where each one breaks - Close the loop with post-training: turn evals into reward functions and RL environments, and catch reward hacking and contamination before they ship - Publish external benchmarks and write-ups on how models perform at real finance work WHAT YOU NEED - Hands-on experience building LLM benchmarks or evals that people used to make real decisions - Strong Python and software engineering; your eval code is reproducible and fast - Statistical judgment: variance, confidence intervals, sample size, inter-rater agreement, and knowing when a delta is noise - Working knowledge of post-training (SFT, RL with verifiable rewards) and how evals feed into it - Comfort with SQL and large datasets; you'll work directly from production traces - Taste for what is worth measuring NICE-TO-HAVE - Industry experience on an eval, benchmarking or post-training team - Built RL environments or verifiers for agentic tasks - Published benchmark or evaluation research (NeurIPS Datasets & Benchmarks, ICML, ACL) - Built or contributed to eval frameworks (verifiers, lm-evaluation-harness, HELM or similar) - Familiarity with accounting, finance or other expert-domain work - Public work we can look at: a benchmark, an eval suite, a failure-analysis write-up BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Isomorphic Labs is looking for a Senior Research Investigator to develop computational models and methods for biologics discovery and therapeutic design.

Isomorphic Labs is looking for a Senior Research Investigator to lead pharmacology strategy and advance biologics candidates through drug discovery.

Isomorphic Labs is looking for a Research Leader in Toxicology to lead safety assessment strategies, regulatory toxicology work, CRO partnerships, and AI-driven toxicology platform development.

Setting grand challenges for your field: identifying long-horizon open problems where Claude falls short of expert judgment, and defining what real progress on them looks like. Applying Claude to open problems in your domain, using test-time compute strategies and integrating the scientific tools and software your field relies on.

Location: Singapore Workplace: Hybrid About Cantina: Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. About the Role: Cantina is expanding, and we're looking for a Research Scientist to join our growing Singapore team! In this role, you will drive foundational research on video generation models, taking ownership across the full research cycle and driving post-training research. Furthermore, you'll collaborate closely with data, infrastructure, and adjacent modeling teams to translate research findings into durable model improvements. What You’ll Do: - Build and maintain scalable systems for ingesting, preprocessing, and delivering large-scale video data for model training - Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes - Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs - Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems - Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency - Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns - Build tooling to support deduplication workflows at scale, including near-dedup pipelines over large video corpora - Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance distillation and adversarial distillation, with a focus on preserving or improving generation quality while reducing inference cost - Develop reward models and preference-based fine-tuning pipelines that align video generation quality with human judgments across dimensions such as aesthetics, motion quality, and prompt adherence - Analyze the relationship between base model behavior and post-training outcomes, and work with the foundation model team to inform pretraining decisions accordingly What You’ll Bring: - Strong hands-on experience building or scaling large-scale data systems or pipelines for machine learning workflows - Experience with distributed data processing frameworks such as PySpark or Ray, and orchestration tools such as Airflow or equivalent - Familiarity with containerization and container orchestration, including Docker and Kubernetes - Experience working with cloud-based data storage and compute (AWS, GCS, and/or Azure), including tradeoffs around cost, throughput, storage layout, and access patterns - Familiarity with video and media processing tools such as FFmpeg, PyAV, DALI, or OpenCV - Familiarity with multimodal or media data, including video, image, text, and audio - Strong research background in post-training methods for large-scale diffusion or flow-based generative models, with deep hands-on experience in distillation across both inference efficiency and quality preservation - Experience with reward modeling or preference-based fine-tuning for generative models, including RLHF, DPO or equivalent alignment approaches - Solid understanding of the interplay between pretraining and post-training, and how base model properties affect distillation and fine-tuning outcomes - Proficiency in Python and modern machine learning frameworks, with a strong preference for PyTorch or JAX - Track record of independent research, with the ability to drive projects from initial idea through experimental validation - Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) preferred - Good understanding of the practical challenges involved in building reliable, scalable, and reproducible data workflows for machine learning systems Benefits We Offer: - Competitive salary and generous company equity - Personal time off and paid holidays - Health insurance - Global travel insurance: Covers you when traveling internationally - Monthly spending stipend: $500 (~S$635) - Equipment: All equipment needed for your home office

Location: Remote - European Region Workplace: hybrid Coinspaid Dev is the engineering brand behind the technology, infrastructure, and R&D expertise built within Coinspaid. With more than 120 engineersand over 11 years of industry experience, Coinspaid Dev brings together software engineering, infrastructure, security and R&D teams with experience building distributed systems and blockchain infrastructure operating across more than 20 blockchain networks. Coinspaid Dev emerges as an independent engineering brand with a straightforward mission: to advance blockchain infrastructure engineering and contribute practical expertise back to the industry. The structure is new. The experience behind it is not. We are looking for a software engineer with deep knowledge of blockchain technologies to develop and support innovative solutions in the field of crypto processing, blockhain, DeFI, dApps and multy-party computations. Your role will involve research, designing, developing, and testing solutions in secure, high-performance distributed systems for processing cryptocurrency transactions, as well as integrating new functionalities into the company's products like MPC, distributed signing, etc. Responsibilities: - Develop and implement innovative solutions for the company's products in the blockchain and crypto-processing sector - Write clean, high-quality, maintainable code - Integrate new functionalities into existing solutions and system architectures - Integrate blockchain platforms with the company's existing systems - Conduct code reviews and ensure code security - Participate in architectural discussions and develop scalable system architectures - Work with APIs to interact with crypto- payment gateways and external services - Analyze system performance, identify bottlenecks, and optimize the system - Participate in the development of new products and services based on blockchain technologies Requirements: - A passion for problem-solving and finding innovative solutions - At least 5 years of experience as a developer - Deep knowledge of programming languages: Go , Solidity - Expertise in Web3 - Experience with version control systems (Git) - Experience working with blockchain platforms (Ethereum, Solana, Tron, etc.) - Experience with cryptographic protocols and smart contracts - Experience with DeFi - Knowledge of decentralized application (dApps) architectures - Understanding the specifics of cryptocurrency transactions and smart contracts - English level: B1-B2 Nice to have: - Experience with financial operations and high-load systems - Knowledge of containerization and DevOps infrastructure (Docker, Kubernetes) - Experience with programming languages: Rust, JavaScript, Python Why join CryptoProcessing by Coinspaid You’ll be joining a company that is actively shaping its space – with enough scale to matter and enough room to make an impact. At Coinspaid, people are expected to think, contribute, and take ownership – and are supported in doing so. We focus on flexibility, wellbeing, and long-term growth – without overcomplicating how benefits work. Flexible Benefits Benefit Bar – up to €230/monthA flexible monthly budget you can use for what matters most to you – from sports and mental health to coworking, home office, or medical-related expenses. Work & Flexibility Fully remote work from almost anywhere Optional offices and relocation support Flexible, async-friendly environment Growth & Learning Budget for courses, certifications, and professional development Language learning support Cross-team learning and knowledge sharing Wellbeing & Support Medical insurance or reimbursement depending on location Access to mental health support Financial support for important life events Extras Merch shop with rewards system Team offsites and company events Sounds good? Well then, we can’t wait to see your resume! To learn more please visit: https://coinspaid.com/about-us/ & https://coinspaid.com/careers/

Location: London Workplace: Hybrid About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Role Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI. Niantic Spatial's R&D Team in London is seeking a Research Scientist to push the frontiers of Spatial AI, advancing our Large Geospatial Model as a dedicated member of the Embodied AI group. You will invent and develop algorithms and systems that sit at the cross-section of computer vision, machine learning, AI, graphics, and robotics. We're passionate about discovery, so we're looking for scientists who want to learn, publish, teach, and deploy every new invention. What You'll Do - Develop New Spatial Representations - Develop new spatial representations that embodied AI can learn from, train in, and act on. - Advance Real-to-Sim Pipelines - Advance real-to-sim pipelines connecting real-world observations, 3D reconstruction, simulation, and robotic policy learning. - Solve Emerging Spatial Problems - Solve emerging problems that relate to spatial intelligence, 3D scene representation, training data, model efficiency, and interactive systems. - Take Capabilities Through to Evidence - Take new capabilities through to evidence: what changed for the policy, not only what changed in the reconstruction. - Build Prototypes - Build proof of concept prototypes, initially for internal use, where some will need to run at production scale on customer data. - Collaborate Across Research and Product - Collaborate closely with researchers, engineers, and product partners to bring novel models to production. - Publish Your Work - Publish and present your work. What You'll Bring - A PhD in computer vision, robotics, graphics, machine learning, or a related field, or equivalent research experience, with a publication record at top venues. - Substantial expertise in robotics or embodied AI, including experience in sim-to-real transfer, domain randomization, policy evaluation, and simulation-based learning. - Deep experience with 3D scene representations: Gaussian splatting, neural fields, meshes, point clouds, or implicit representations. - You have applied generative or diffusion models to 3D data, such as scene completion, inpainting, relighting, or novel view synthesis from sparse capture. - You have worked on 3D semantics, open-vocabulary scene understanding, or scene graphs. - Proficiency in Python and Deep Learning libraries such as PyTorch. - GPU or graphics programming experience, such as custom CUDA kernels, shaders, or differentiable rendering. - A track record of contributing to widely used open-source research codebases. Nice to Have - Proficiency in C++. Compensation & Benefits Base salary range of £103,500 to £202,500. Compensation also includes an annual bonus, equity, and a comprehensive benefits package. Location & Work Model This role is based in our London office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: Singapore Workplace: Hybrid About Cantina Cantina Labs is a social AI company developing a suite of advanced video generation models. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. About the Internship Cantina is growing its research lab in Singapore, and we are looking for exceptional research interns to work with us on the next generation of video models in October 2026. This is a three month onsite internship designed to give you meaningful ownership of a well-defined research or engineering problem. You will be matched with a project based on your background and interests, working closely with a senior mentor from initial problem formulation through experimentation, evaluation, and, where appropriate, submission to a leading AI conference. Projects may focus on post-training and inference efficiency for video generation models, reward modeling and preference-based optimization multimodal data systems, or scalable infrastructure for video model training. The primary focus will be your core project, with opportunities to contribute to applied or product-adjacent work where relevant. What You’ll Work On Depending on your project, you may: - Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance and adversarial distillation - Explore techniques that reduce inference cost while preserving or improving generation quality - Develop reward models and preference-based optimization methods to improve aesthetics, motion quality, temporal consistency, and prompt adherence - Study how base-model behavior affects post-training outcomes and use experimental findings to inform model development - Design rigorous evaluations and conduct large-scale experiments on generative video models - Contribute to evaluation harnesses, model integrations, research tooling, or other product-adjacent projects related to your core work - Document and communicate your findings through research reports, internal presentations, demonstrations, and potential conference submissions You may be a good fit if you - Are currently pursuing a PhD or are a final-year master’s student in computer science, machine learning, computer vision, or a related field - Have research experience in generative modeling, computer vision, multimodal learning, or video generation - Have hands on experience with diffusion models, flow-based models, model distillation, reinforcement learning, preference optimization, or related post-training techniques - Can formulate hypotheses, design controlled experiments, analyze results, and communicate conclusions clearly - Are proficient in Python and have hands-on experience with PyTorch, JAX, or another modern machine learning framework - Are comfortable working independently on an open-ended research problem while collaborating closely with a mentor and the broader team Experience with video, image, audio, or other multimodal data is valuable. Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or AAAI are a plus, but are not required. We care most about the quality of your thinking, the depth of your technical work, and your ability to learn quickly. What You Can Expect - A defined project and named senior mentor before your first day - Weekly one-on-one meetings and clear project milestones - A meaningful compute allocation for your research - The opportunity to own a complete research or engineering result - First-author positioning by default where your contribution supports a publication - Timely internal review of research intended for submission - Support for conference travel if your paper is accepted - Opportunities to demonstrate your work and receive credit for product contributions - A competitive monthly stipend - Visa and travel support for eligible international candidates - Housing support for qualifying international interns in Singapore - Equipment and resources needed to complete your work Internship Details - Location: Singapore - Duration: Three months - Working model: Onsite - Start dates: First batch starts in October 2026, second batch starts in January 2027

Location: San Francisco; Sunnyvale Workplace: Hybrid About Cantina: Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future! About the Role: We are building a core team to develop next-generation native video and omni foundation models for multimodal generation and understanding. Our current focus is large-scale video foundation model development, spanning pre-training, continued training, and post-training for high-quality, controllable, consistent, and efficient generation. Our broader roadmap includes reference- and memory-based generation, multimodal understanding and interaction, and joint audio-video generation. In this role, you will work on foundational research and large-scale model development across the full model lifecycle, including architecture, data, training, evaluation, post-training, training systems, and efficient inference. You will have the opportunity to shape both the technical direction and the team from an early stage. What You’ll Do: - Research, develop, and scale native video and multimodal foundation models, from early prototypes through large-scale pre-training, continued training, and post-training. - Explore new model architectures, training objectives, and conditioning mechanisms for video generation, reference- and memory-based generation, multimodal interaction, and joint audio-video generation. - Build and improve large-scale data curation, distributed training, evaluation, and post-training pipelines for high-quality and controllable generation. - Design systematic experiments to understand model scaling, generation quality, controllability, consistency, robustness, and inference efficiency. - Collaborate closely with researchers, engineers, and product teams to help shape the technical roadmap and, where appropriate, translate model advances into real-world capabilities. - Contribute to research publications and open-source releases when appropriate. What You’ll Bring: - Strong research and engineering experience in generative modeling, including areas such as diffusion models, flow matching, DiTs, video generation, multimodal models, world models, or related fields. - Hands-on experience training and evaluating large-scale image, video, or unified multimodal models using modern deep learning frameworks and distributed training systems. - A strong track record of developing impactful models or systems, demonstrated through research publications, open-source contributions, production impact, or other significant technical work. - Ability to independently own ambiguous research problems, move effectively from ideas to experiments, and work well in a highly collaborative environment. - Specialized depth in one or more areas across the foundation model lifecycle, such as model architecture, data curation, controllable generation, multimodal understanding and conditioning, post-training and reward modeling, model acceleration, inference systems, or deployment. Compensation: The anticipated annual base salary range for this role is between $200,000-$320,000. When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data. Benefits: - Competitive salary and generous company equity - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support - 401(k) retirement savings plan - Lifestyle spending account – $500/month to use however you’d like - Complimentary lunch and snacks for in-office employees - One Medical membership, and more!