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Location: Warsaw, Poland Are you looking for the next professional opportunity that will challenge you and advance your career? Join our team now! Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact. About Tickmill. Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures & Options). Founded in 2014, the Tickmill Group employs over 330 professionals across offices in London, Cyprus, Poland, Estonia, Seychelles and several other locations worldwide. Our culture is built on trust, transparency, and high standards.  We bring together ambitious professionals who are looking for an environment that supports them to dominate in their fields. Our diverse, multilingual teams are focused on innovation, delivering bold excellence, and raising the bar wherever we can. We offer competitive benefits packages, frequent team initiatives, and opportunities for professional growth. Join the Tigers! What does the role look like? The Senior AI/ML Engineer (AI Lead) will have the chance to: • Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments. • Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact. • Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management. • Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies. • Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts. • Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact. • Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations. • Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness. • Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery. • Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery. What do you need to succeed in this role? • 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation). • Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability). • Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability). • Hands-on experience with large-scale data processing (Spark / PySpark). • Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility. • Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows. • Strong SQL skills and experience working with large, complex datasets in real-world environments. • Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements. • Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems. • Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly. • Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness. The below are considered as a plus: • Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions. • Experience with real-time or streaming ML systems. • Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data. • Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards). • Experience contributing to team standards, mentoring, or leading applied AI delivery. By joining us, you can expect: • Unique opportunity for a career in a global, fast-growing company. • Attractive remuneration package based on qualifications and experience. • Opportunities to learn and grow through our “Employee Training & Development program”. • Hybrid work flexibility. • Multiple events to bond with the team and the group. • Birthday and loyalty benefits. Make your next career step and apply NOW! *Due to the great number of applications, we receive for each of our open vacancies, we are unable to respond on an individual basis.

Location: Воронеж Мы ищем редактора (AI-тренера), который станет наставником для GigaChat. Если ты любишь работать с текстами, видишь красоту в деталях и мечтаешь влиять на технологии будущего — это твой шанс. Обязанности * создавать и редактировать тексты на самые разные темы * анализировать ответы GigaChat, находить ошибки и улучшать логику его мышления * разрабатывать сценарии диалогов и тестовые задания * искать оригинальные источники, проверять факты и доводить текст до совершенства * тестировать новые версии модели и давать фидбэк разработчикам. Требования * получил высшее лингвистическое, журналистское или переводоведческое образование * имеешь опыт работы с текстами - копирайтер, технический писатель или любые подобные должности * обожаешь детали: твоя сверхспособность — находить нюансы даже в идеальных предложениях * легко ориентируешься в больших объёмах информации и быстро погружаешься в новые темы. Будет круто, если у тебя есть: * опыт работы с LLM (ChatGPT, GigaChat, DeepSeek и пр.) * навыки написания промтов * понимание технической терминологии (JSON, Markdown) * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия * работа в удалённом формате для Воронежа и Воронежской области * ежегодный пересмотр зарплаты и годовая премия * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера..

Требования: - отличное владение русским языком - внимательность, аналитичность - способность работать по инструкции Обязанности: - проверять соответствие описания видео - вносить корректировки и уточнения - следовать инструкциям и метрикам качества Условия: - оплата от 30 000 рублей - гибкий график: 5/2, 6/1, 7/0 - работать можно в любое время - норма — от 6 часов в день - компания компенсирует налоги - удалённая работа - возможность карьерного роста

Location: London, England, United Kingdom Graham Capital Management, L.P. (collectively with its affiliates, "Graham") is an alternative investment manager founded in 1994 by Kenneth G. Tropin. Specializing in discretionary and quantitative macro strategies, Graham is dedicated to delivering strong, uncorrelated returns across a wide range of market environments. As one of the industry’s longest-standing global macro and trend-following managers, Graham remains committed to innovation, evolving its strategies through a robust investment, technology, and operational infrastructure. Graham harnesses the synergies between its discretionary and quantitative trading businesses to offer a broad suite of complementary alpha strategies, each built on the principles of thoughtful portfolio construction, active risk management, and diversification by design. Graham invests significant proprietary capital alongside its clients – including global institutions, endowments, foundations, family offices, sovereign wealth funds, investment management advisors, and qualified individual investors – reinforcing alignment of interests across all strategies. The foundation of Graham’s sustainability and success is the experience and contributions of its people. The firm seeks to cultivate talent, encourage the diversity of ideas, and respect the contributions of all. In turn, each employee shares in the responsibility of strengthening those around them. Description Graham is seeking a ML Engineer to join our Data Science team, a future-looking technical arm of Graham. We envision, design, prototype and implement the processes that feed Quantitative Research and Discretionary Trading teams as well as the broader firm. We are passionate about what we do and welcome every opportunity to prove it. The Data Science department straddles traditional Data Science and Engineering roles as well as the application of Machine Learning & AI. We work closely with Quant Researchers, Portfolio Managers, Operations and Execution to continuously improve upon our offering. Every day we work to transform our business through data, technology, and the insights we provide our stakeholders. At Graham, our systems feed live models around the clock, span billions of market data ticks, an ever-increasing corpus of news and other texts as well as a broad spectrum of financial and alternative data. Our objective is to support the research process by providing our stakeholders with all the right pieces to succeed in their jobs. Responsibilities You will be part of a growing team within Data Science. You will work alongside world-class talent to find innovative solutions to some of the most interesting problems on the buy-side. You will work closely with other areas such as Technology, Quantitative Research and Portfolio Manager groups as well as Risk and Operations to learn about problems they face with respect to data and ultimately develop cutting edge solutions. Your focus will be to dive deep into multiple data sets to understand relationships, develop time series, forecasting models, and support quant strategies, and provide new insights and leverage state-of-the-art machine learning and advanced statistical methods to produce the best data sources for the fund. Requirements Undergraduate or higher degree in Computer Science, Engineering, Operations Research, or other quantitative discipline 3+ years of hands-on experience with Machine Learning and Statistics on large, unstructured, data sets Experience writing production code for multi-client systems serving model results is a great plus Ability to clearly communicate research findings to technical and nontechnical stakeholders Full-stack experience with Python (preferred) or C++, Spark/Scala, SQL or other distributed data processing technologies as well as experience working comfortably building and deploying services and models in containerized environments Experience with scientific computing, statistics, optimization, time series, panel data, etc. Comfortable handling multiple projects to solve varied problems working with multiple teams Detail-oriented mindset Sense of ownership of his/her work, working well both independently as well as collaboratively This role requires commuting into our London office Mondays through Fridays.

Location: Canada; Europe; United States; United Kingdom Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? Ship state of the art models to production. Design and implement novel research ideas. Build elegant training/deployment pipelines. Join us at a pivotal moment, shape what we build and wear multiple hats as an intern! Our recruitment process will begin in the upcoming weeks, and we will be carefully reviewing applications and assessing potential candidates for our internships. Should we find a suitable match with your qualifications and our requirements, we will be in touch to discuss the opportunity further and to advance your application to the next stage Please Note: To be eligible for this position you should be a student currently enrolled in a post-secondary program, available for a full-time 3-6 month internship, co-op, or research work term. As a Machine Learning Intern, you will: - Design, train and improve upon cutting-edge models. - Help us develop new techniques to train and serve models safer, better, and faster. - Train extremely large-scale models on massive datasets. - Explore continual and active learning strategies for streaming data. - Learn from experienced senior machine learning technical staff. - Work closely with product teams to develop solutions. You may be a good fit if you have: - Proficiency in Python and related ML frameworks such as Tensorflow, TF-Serving, JAX, and XLA/MLIR. - Experience using large-scale distributed training strategies. - Familiarity with autoregressive sequence models, such as Transformers. - Strong communication and problem-solving skills. - A demonstrated passion for applied NLP models and products. - Bonus: experience writing kernels for GPUs using CUDA. - Bonus: experience training on TPUs. - Bonus: papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP). FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: Brazil; United Kingdom; Pakistan; Philippines; South Africa About Huzzle At Huzzle, we connect high-performing professionals with global companies across the UK, US, Canada, Europe, and Australia. Our clients include startups, digital agencies, and tech platforms in industries like SaaS, MarTech, FinTech, and EdTech. We match top talent to full-time remote roles where they’re hired directly into client teams and provided ongoing support by Huzzle. Role Type: Full-time Engagement: Independent Contractor About the Company Our client is a fast-growing AI startup building intelligent AI employees that can call, text, email, and update CRMs automatically. Their platform helps businesses across North America automate sales follow-up, lead engagement, and customer communication at scale while maintaining a natural, human-like experience. This is a lean, high-performance startup environment where team members work closely with leadership, take ownership, and directly influence product growth and client success. Job Summary We’re looking for an Automation Engineer who thrives at the intersection of AI, workflow automation, prompt engineering, and client problem-solving. In this role, you won’t just build automations, you’ll design and optimize AI-driven systems that directly impact revenue for real businesses. You’ll work closely with the founder to create AI agents capable of handling calls, texts, emails, and CRM updates while ensuring conversations feel natural and high-converting. This is an ideal opportunity for someone who enjoys building systems, solving technical problems, and working directly with clients in a fast-moving startup environment. Key Responsibilities Build and deploy AI-powered agents that call, text, and email leads on behalf of clients Create and optimize workflow automations using platforms such as n8n, Make, or similar tools Integrate AI systems with CRMs like HubSpot, Zoho, Salesforce, and GoHighLevel using APIs and webhooks Design prompts and conversational flows that make AI interactions feel human and sales-focused Troubleshoot automation issues, debug workflows, and improve system performance Handle client onboarding and support requests directly Collaborate closely with the founder on new automation strategies and feature improvements Analyze workflow performance and continuously optimize AI behavior and conversion rates Maintain documentation for automations, integrations, and client implementations Requirements Hands-on experience with workflow automation tools such as n8n, Make, Zapier, or similar platforms Strong understanding of APIs, webhooks, JSON, and third-party integrations Experience reading technical API documentation and troubleshooting integration issues Familiarity with CRM systems including HubSpot, Salesforce, Zoho, or GoHighLevel Experience with prompt engineering for conversational AI or sales-focused AI workflows Understanding of outbound sales workflows, SMS campaigns, follow-up automation, and lead nurturing Excellent problem-solving and debugging skills Comfortable working directly with clients in a fast-paced startup environment Strong communication skills in English Ability to manage multiple projects and adapt quickly to changing priorities Experience building AI voice agents or AI employees Experience with OpenAI APIs, conversational AI platforms, or LLM workflows Knowledge of sales psychology and high-converting sales scripts Previous startup experience Basic scripting or coding experience (JavaScript, Python, etc.) Benefits 💻 Fully Remote: Work from anywhere with international teams 🚀 Career Growth: Join companies in SaaS, MarTech, and B2B services 🤝 Peer Community: Connect with high-performing sales professionals in our network 🧭 Ongoing Support: Receive guidance from Huzzle before and after placement 💰 Tailored Compensation: Salaries vary by client and candidate preference — we’ll match you with options that fit your goals

Location: San Francisco Workplace: OnSite ABOUT HUMAN ARCHIVE Human Archive is a research lab focused on modeling human embodied intelligence. Humans are the most sophisticated biological systems we have ever observed, yet we still do not fully understand ourselves. Research into human physical intelligence — including the human hand, proprioception, and vision — remains largely unsolved. Our mission is to recover human embodied intelligence as a learned model. To achieve this, we build custom hardware products, deploy them globally at scale, and publish research. Today, our data is used for robotics and world modeling, but the broader opportunity is advancing scientific research into intelligence itself. Founded by Stanford and UC Berkeley researchers, we are lean, deeply technical, and operate at extreme speed, taking on unglamorous and conventionally impossible problems that directly unlock step-function gains in model capability. The deployment of capable humanoids at scale will permanently redefine human labor. Undesirable physical work will disappear, and human effort will shift toward a new era of abundant creativity. We are building the infrastructure to accelerate that transition by assembling the Human Archive mafia. You will own meaningful systems from day one and see your work directly impact model capabilities. This is a once-in-a-generation inflection point. If you want to help reshape physical labor and work on problems that matter at civilizational scale, join us. THE OPPORTUNITY As a Machine Learning Engineer, you’ll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the intersection of applied machine learning, data infrastructure, and robotics, where your work directly shapes how data is collected, validated, annotated, and evaluated. You’ll help close the loop between research and data collection by fine-tuning VLAs on downstream policy performance and building post-training and reinforcement learning systems around real-world robotics tasks. You’ll be expected to make architectural decisions, own projects end-to-end, and operate in highly ambiguous research environments given the novelty and scale of our multimodal datasets. Your work will help shape how frontier labs and leading robotics companies train their models, transforming physical labor markets and economies while contributing to broader research into human embodied intelligence. WHAT YOU’LL DO - Build systems for multimodal perception, annotation, dataset QA, and robotics evaluation - Publish research on multimodal data by fine-tuning and evaluating VLA models on downstream robotics tasks and policy performance - Build post-training and reinforcement learning systems around robotics failure modes and corrective demonstrations - Work across video understanding, tracking, pose estimation, temporal modeling, and multimodal alignment - Develop tooling for benchmarking, observability, and temporal efficiency - Prototype quickly, ship rapidly, and iterate from real-world robotics deployments and research feedback WHAT WE’RE LOOKING FOR - Passionate, mission-driven individuals who have demonstrated exceptional ownership in previous work - Engineers who want their work to directly impact the next frontier of physical AGI - Strong ML engineering fundamentals across robotics, computer vision, and perception systems - Experience with video understanding, tracking, pose estimation, robotics, or real-world sensor systems - Strong technical intuition and ability to move quickly in ambiguous research environments - Published research or production experience in robotics, embodied AI, reinforcement learning, motion capture, or vision systems is a strong plus

Location: Canada; San Francisco; Europe; London; United States; United Kingdom; Toronto; Montreal Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? To have the opportunity to collaborate with Cohere researchers and tools on designing and implementing novel research ideas and shipping state-of-the-art models to production. We have openings in teams covering base model training, retrieval augmented generation, data and evaluation, safety, and finetuning, to name a few; and we are open to receiving intern applications in any research area relating to LLMs to broaden your research connections while obtaining deep experience in a growing AI startup.  Please Note: To be eligible for a Research Internship, you must be currently pursuing a PhD in Machine Learning, NLP, or a related discipline. You need to be available for a full-time internship that lasts for 4-6 months. As a Cohere Research Intern, you will: - Conduct cutting-edge machine learning research, building and training large language models.  - Focus on research projects aimed at expanding the frontier of knowledge in language modelling and associate areas such as evaluation, multimodal models, optimisation etc. - Disseminate your research results through the production of publications, datasets, and code. - Contribute to research initiatives that have practical applications in Cohere’s product development.  You may be a good fit if you: - Are currently pursuing, or in the process of obtaining, a PhD in Machine Learning, NLP, Artificial Intelligence, or a related discipline. We will also consider exceptional non-PhD candidates. - Are eligible for work authorization in the country of employment at the time of hire and maintain ongoing work authorization throughout the internship period.  - Have experience using large-scale distributed training strategies, data annotation and evaluation pipelines, or implementing state of the art ML models. - Are familiar with autoregressive sequence models, such as Transformers. - Have strong communication and problem-solving skills with the ability to convey complex research findings clearly and succinctly.  - Have knowledge, or are knowledgeable, of programming languages such as Python, C, C++, Lua, or related languages. - Have knowledge of related ML frameworks such as JAX, Pytorch and Tensorflow. - Have previous experience in building systems based on machine learning and deep learning techniques.  - Demonstrate passion for applied NLP models and products. Preferred Qualifications: - Demonstrated expertise through publications in top tier venues in fields such as machine learning, NLP, artificial intelligence, computer vision, optimization, computer science, statistics, applied mathematics, or data science.  - Proven ability to tackle analytical problems using quantitative methodologies.  - Proficiency in handling and analysing complex, high-dimensional data from various sources. - Experience in applying theoretical and empirical research to real-world problem-solving. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: Europe; United States Workplace: Remote ABOUT CANTINA Cantina is a new social platform founded by Sean Parker with the most advanced AI character creator. Our bots are lifelike, social creatures that can interact wherever people are online—across voice, video, and text. Create yourself, imagine someone new, or choose from thousands of characters to share infinitely scalable, personalized content and seamless group chat. If you’re excited about how AI can shape creativity and social interaction, come help us build what’s next. ABOUT THE ROLE: We’re looking for a Research / ML Engineer to join our Speech Team to build state-of-the-art speech systems end-to-end—from data specs through production inference. You’ll drive the model ↔ data ↔ eval flywheel for TTS and adjacent tasks (voice cloning, controllable TTS, voice conversion and more), partnering closely with research, data, and infra to ship fast, reliable, and cost-aware models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. WHAT YOU’LL DO: - Model Building: Architect, implement, pre-train, fine-tune, and post-train/alignment (e.g., GRPO/DPO) for large-scale speech models. - Project Leadership: Independently lead small research projects while collaborating on larger team initiatives. - Experimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models. - Tool Development: Develop and improve dev tooling to enhance team productivity. - Full-Stack Contribution: Contribute to the entire stack, from low-level optimizations to high-level model design. - Data Ownership: Define data requirements and collaborate on acquisition, curation, augmentation, labeling quality, and synthetic data strategies. - Rigorous Evaluation: Design automated objective/subjective evaluations—listening tests, SV/WER/ASR-based metrics, robustness & bias checks, and red-team studies. - Pipeline Delivery: Harden the training → evaluation → inference pipeline; profile latency, memory, and cost; and meet production SLAs with robust monitoring and rollback. - GPU Scaling: Partner with infrastructure to run distributed training/inference on cloud fleets and productionize models with reliability and observability. - Safety & Responsibility: Contribute to safety/consent guardrails and to misuse/abuse mitigation for responsible speech technology. WHAT YOU’LL BRING: - Exceptional research/development experience with large scale audio models (>3B models and >500k hours data). - Exceptional understanding and hands-on experience with transformer architectures and/or diffusion models (inc. distillation and streaming) and/or audio language modelling. - Strong experience with multi-node and multi-gpu distributed model training. - Strong software engineering skills with a proven track record of building complex systems - Strong with PyTorch and performance work (profiling, CUDA/Triton/C++ as needed) and writing reliable production quality code. - Shipped large scale speech/audio models to production. - Background in working with large-scale ML data. - Ability to iterate on data,, and triangulate quality using subjective and objective signals. - Notable publications and/or open source contributions in speech/audio/ML. - Experience with voice-cloning, speech-control, voice-generation. PREFERRED EXPERIENCE: - Shipped large scale speech/audio models (TTS/VC/ASR) to production. - Work on large-scale ML systems. - Experience with audio language modelling, transformer architectures. - Experience with voice-cloning, speech-control, voice-generation. - Background in processing large-scale ML data. - Publications or notable open-source in speech/audio/ML. COMPENSATION: When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.   BENEFITS FOR U.S.-BASED ROLES: - 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!

Location: Europe; United States Workplace: Remote 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 seeking an experienced Machine Learning Engineer (MLE) to focus on audio model evaluation, specifically for speech generation and recognition models. This role involves designing and developing comprehensive model evaluation pipelines for both development and production environments, as well as creating automated dashboards for reporting evaluation results. As the founding member of our evaluation team, the ideal candidate is expected to leverage their experience to lead our evaluation efforts and play a key role in the future growth of the evaluation team. WHAT YOU’LL DO: - Designing model evaluation pipelines for models in development and production - Designing user studies for subjective model evaluations. - Converting requirements into measurable metrics. - Designing and developing automated evaluation dashboard to see model performances and compare results. - Training new models to capture new and different evaluation metrics. - Communicating with the model team to help design better models based on the evaluation results. - Communicating with the data team to help decide the type of data necessary to improve model performance. - Communication with the product-manager to make sure product requirements are correctly measured. - Help grow the evaluation team as the founding member. - Lead the evaluation team in the future. WHAT YOU’LL BRING: - Strong experience and intuition for designing metrics that capture model performance. - Strong experience with designing user studies on Mechanical Turk or similar platforms. . - Strong experience with model training and fine-tuning for model evaluation. - Strong statistical knowledge and experience to statistically compare evaluation results and take decisions. - Very strong engineering and programming skills. - Experience with training ASR, TTS models. - Experience at ML teams working on large-scale machine learning problems. (>3B models with >1m hours of data) COMPENSATION: When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.   BENEFITS FOR U.S.-BASED ROLES: - 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!

Location: London, Singapore DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. The Machine Learning Researcher will have a deep understanding of the principles behind modern ML algorithms — including recent advances such as transformer-based architectures and other state-of-the-art frameworks — and the ability to turn that knowledge into high-impact, production-ready solutions. The role involves applying advanced ML techniques to a wide range of forecasting challenges, building scalable ML pipelines, and deploying them in production, while working with high-dimensional, noisy, structured and unstructured datasets. Experience applying ML models in financial markets is desirable, but exceptional candidates with a strong ML background from tech, startups, or academia will also be considered. Responsibilities Research, design, and deploy robust ML models. Build and maintain scalable, production-level ML pipelines. Extract signals from large, noisy, real-world datasets. Qualifications PhD (or exceptional MSc) in ML, Computer Science, or related field. Deep theoretical and practical knowledge of core ML algorithms, and comfortable experimenting with model architectures, feature engineering, and hyperparameter tuning to produce high-performance and resilient models. Proven experience taking ML models from research to live production. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . [#LI-AC]

Location: San Francisco, CA | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. Decisions in production training depend on knowing how the model is really behaving. Our team's mission is to have the best observability into production post-training runs at Anthropic: to be the clearest, most trusted view of model quality during training. We shape what gets measured, keep the signal trustworthy and timely, and get to the bottom of surprising results to inform post-training and release decisions. In this role you will lead the science of how we evaluate production training runs. You'll work out which measurements tell us something real about the model, notice when they stop doing so, and find what we should be measuring but aren't. You'll partner with research teams across every RL domain, bringing their priorities into what we measure and setting the standard for what makes an eval trustworthy across post-training. You'll also be hands-on with the eval fleet day to day, because the best questions about measurement come from watching real results come in during a live run. Responsibilities Steer the eval strategy for production Claude models: study and optimize the eval mix so it gives the most accurate and complete assessment of model quality Run and monitor the eval fleet live to understand how each Claude model is developing as it trains Advise teams across post-training on eval methodology, and help eval authors bring new evals up to the bar for production Investigate regressions in production runs and inform training interventions when appropriate Build the dashboards, alerts, and reports that researchers and leadership use to track model quality You may be a good fit if you Have designed, run, and analyzed evaluations for ML models at scale Care deeply about measurement quality, thinking twice before trusting a number Can turn ambiguous results into clear recommendations, and are comfortable influencing direction across teams Have strong Python skills and are comfortable working with production systems Maintain clarity and rigor when debugging complex, time-sensitive issues Thrive in controlled chaos and are energized, rather than overwhelmed, when juggling multiple urgent priorities during a live training run Care about the societal impacts of your work and about shipping frontier models responsibly Strong candidates may also have Hands-on experience post-training large language models Research experience in ML evaluation or benchmarking Background in statistics and experimental design Experience developing robust evaluation metrics for ML systems The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Athens Workplace: Hybrid About the company: KIEFER is building Greece's integrated AI ecosystem. From renewable energy infrastructure and AI systems to robotics and enterprise applications, we connect the technologies that power Europe's intelligent future. Founded in 2014, KIEFER has delivered 600MW+ of energy projects and is now developing sovereign AI infrastructure, enterprise AI products and physical AI systems for Greece and Southeast Europe. About the role: We are looking for an ML Team Lead to join Kiefer Tech and lead our ML Engineering team. In this role, you will lead the development and continuous improvement of Sophea AI, our Greek-focused Large Language Model. You will work across LLM training, fine-tuning, ASR, inference optimization, model validation, and production-grade ML systems. Beyond the model itself, you will help shape the ML Engineering function at Kiefer Tech: define clear ownership, strengthen team workflows, raise technical standards, and ensure complex ML initiatives are delivered with accountability, clarity, and strong execution. What you will do: - Lead the ML Engineering team working on Sophea AI and set a clear technical direction for its development - Own delivery across core ML workstreams, including LLM training, fine-tuning, ASR, inference optimization, model validation, and production-grade ML systems - Build a stronger operating model for the ML function: improve workflows, clarify ownership, strengthen decision-making, and remove execution blockers - Turn the ML roadmap into clear priorities, accountable workstreams, and measurable outcomes aligned with product and business goals - Mentor ML engineers, develop team capabilities, and bring stronger engineering practices, tools, and research-driven approaches into the team’s daily work What you will need: - Proven experience leading ML Engineering teams or complex ML workstreams in high-performance, fast-moving environments - Deep technical background in LLMs, ASR, and production-grade ML systems, including pre-training, training from scratch, fine-tuning, reinforcement learning, model validation, and performance improvement - Strong understanding of inference optimization, model quantization, and serving frameworks such as vLLM, SGLang, NVIDIA Triton, NVIDIA Dynamo, or similar tools - Ability to bring structure into ambiguous environments: clarify ownership, improve workflows, and drive execution across several ML workstreams - High autonomy, strong communication skills, and the ability to follow current AI research and translate relevant tools, methods, and trends into team practice Nice to have: - Experience with MLOps infrastructure, model serving pipelines, GPU workload management, and experiment tracking - Contributions to open-source ML projects or published research in AI/ML - Familiarity with real-time inference systems or event-driven ML architectures What is there for you: - Compensation: highly competitive package aligned with AI talent benchmarks across Europe and the US - Ownership: high-impact leadership role with real influence on the ML Engineering function at Kiefer Tech - Work format: remote work option, with relocation support available for candidates open to working from our Athens office - AI-native environment: real challenges across LLMs, ASR, GPU workloads, and advanced AI product development - NVIDIA ecosystem: access to related conferences, certifications, internal knowledge sharing, and advanced AI infrastructure through Kiefer’s strategic collaboration - Culture: engineering-first, high autonomy, low bureaucracy, and space to shape meaningful AI products

Location: London, UK About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £370,000 — £630,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Remote-Friendly (Travel-Required) | San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: New York; San Francisco ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models toward higher throughput and lower latency, we’d love to hear from you! YOU MAY BE A FIT IF: - You have 5+ years of experience writing high-quality, high-performance code - You’ve worked with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT) - You’re familiar with NVIDIA GPU architecture and CUDA - You have experience with ML performance engineering (tell us a story about boosting GPU performance: debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc.) NICE-TO-HAVE: - Familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc.)

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a strong Robotics Learning Engineer to join our Manipulation team based in London. In this role, you will work on teaching our robots to manipulate the world around them. This is a role at the intersection of applied deep learning and robotics, and to be set up for success you need both experience of working with real robot hardware (e.g. identifying issues in control or teleop), and applied deep learning (you don’t have to be an expert on cutting edge neural network techniques, but you should be perfectly capable of curating data, fine-tuning a policy on that data and hypothesising potential mitigations when something doesn’t work). WHAT YOU’LL DO - Post-train manipulation policies via behaviour cloning and RL; own the full loop from data to deployment. - Come up with data preprocessing strategies to improve the quality of collected data. - Work with the simulation team to set up RL training using digital twin, and then iterate on reward and simulation quality to ensure successful transfer to the real world. - Partner with the data collection organization to drive data collection activities for a specific capability: specify what good data looks like, ensure diversity and coverage, and iterate on instructions. - Expand observation and action spaces with new components required to support novel capabilities, and work with the Teleoperations team to expose these components to robot operators. - Partner with Teleoperations and Controls teams to improve motion smoothness and teleoperation experience. - Interface with hardware design team to ensure that manipulation team findings regarding the current generation of hardware are reflected in future designs. WHAT WE'RE LOOKING FOR - 3+ years working on robots (industry or research) with shipped artifacts to show for it. A good understanding of modern teleoperation and low-level control stack. - Experience with neural network post-training. - Familiarity with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training, PyTorch or JAX. Ability to profile & debug numerics and write maintainable research code. - Good familiarity with modern software engineering practices. - Ability to document experiments clearly and communicate trade‑offs crisply. Nice to have: - Experience training VLA models for manipulation (autoregressive, diffusion or flow-matching based). Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks. - Experience applying RL to robotics problems. - Publications at top-tier robotics or deep learning conferences or equivalent open‑source contributions. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a Senior Dev/MLOps Engineer to join our Data & Compute Platform team based in London. WHAT YOU’LL DO - Own and manage the full lifecycle of both ML models and core infrastructure - from development and deployment to monitoring and continuous improvement. - Build and maintain robust CI/CD pipelines for both software and ML workflows. - Ensure reliability, scalability, observability, and security of production systems and ML infrastructure. - Automate deployment, orchestration, and environment management using modern DevOps tooling. - Collaborate closely with software engineers, data scientists, and product teams to bring ML-powered features to production. - Proactively detect, troubleshoot, and resolve infrastructure and model performance issues. - Stay up to date with industry best practices in DevOps, MLOps, and infrastructure engineering. - Document infrastructure, workflows, and operational procedures clearly and thoroughly. WHAT WE'RE LOOKING FOR - Proven experience in a senior-level DevOps, MLOps, or related infrastructure-focused engineering role. - Strong proficiency in Python and familiarity with ML frameworks such as TensorFlow or PyTorch. - Deep experience with cloud platforms (AWS, GCP, or Azure) and container orchestration tools (Docker, Kubernetes). - Solid understanding of CI/CD systems (e.g., GitHub Actions, GitLab CI, ArgoCD) and infrastructure-as-code tools (e.g., Terraform, Helm). - Familiarity with data engineering concepts such as ETL pipelines, data lakes, and large-scale batch/stream processing. - Ability to design scalable, secure, and observable systems in fast-moving environments. - Strong debugging and problem-solving skills across distributed systems. - Excellent collaboration and communication skills, with experience working in cross-functional teams. - Understanding of security and compliance best practices for both software and ML systems. WHAT WE OFFER - Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays. - Fully funded private healthcare for UK employees, with broad provider access, virtual and in‑person care, and strong mental health and serious illness support. - Equity included–we believe builders should share in what they build. - Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in‑office. - Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics. - Freedom to influence the product and own key initiatives.