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Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения

Задачи: — разрабатывать и внедрять современные решения в области компьютерного зрения Требования: — опыт коммерческой разработки в области Computer Vision от 3 лет — хорошее понимание классического машинного обучения, статистического анализа, классических алгоритмов Computer Vision — практический опыт работы с нейросетевыми фреймворками PyTorch — умение читать научные статьи, воспроизводить решения и адаптировать их под реальные задачи — уверенное владение библиотеками NumPy, scikit-learn, Matplotlib, OpenCV — уверенная работа с Linux (предпочтительно Ubuntu, Mint или Debian) — опыт контейнеризации с использованием Docker и Docker Compose — уверенное владение Git — английский язык — Upper-Intermediate (B2) или выше (обязательно) — наличие проектов, которые были самостоятельно доведены до production Будет преимуществом: — опыт разработки систем видеоаналитики и/или видеонаблюдения — опыт оптимизации скорости инференса моделей с использованием ONNX Runtime и TensorRT — практический опыт работы с Graph Neural Networks (GraphNN/GNN) и Metric Learning — опыт работы с Kubernetes (K8s) Мы ищем человека, который: — любит решать сложные инженерные задачи — следит за современными исследованиями в области AI и Computer Vision — умеет самостоятельно принимать технические решения и доводить проекты до результата — готов работать над задачами полного цикла — от исследования и прототипирования до внедрения в production

Задачи: — Проектировать и развивать модели компьютерного зрения и видеоаналитики (детекция, трекинг, распознавание лиц, анализ поведения); — Интегрировать ML-модели в production (backend, edge); — Обучать и дообучать модели (fine-tuning, transfer learning); — Работать над реальными AI-проектами с бизнес-применением; — Сопровождать полный цикл разработки: от исследования до деплоя; — Оптимизировать модели и повышать качество CV-решений. Требования: — Уверенные знания Computer Vision и современных DL-подходов; — Глубокое понимание машинного обучения и нейронных сетей (CNN, detection/segmentation); — Уверенное владение Python; — Опыт работы с OpenCV, NumPy, PyTorch и/или TensorFlow; — Опыт работы с Git; — Опыт оптимизации моделей (speed/accuracy trade-offs, inference); — Опыт решения задач детекции, трекинга и анализа поведения людей. Условия: — Формат работы: офис, Астана.

Location: Vietnam The Company Our client is a financial technology (FinTech) company specifically focused on bridging the gap between man and machine when it comes to investing. They are building an AI-driven trading and investing platform covering stocks, futures, forex, and crypto. They develop quantitative strategies, portfolio models, copy trading solutions, and low-latency execution systems. Our client aims to be the world's most trusted AI trading platform where sophisticated market intelligence meets effortless execution, enabling every trader to compete with institutional-level strategies while maintaining full control over their investment decisions. The Role Our client is seeking a Senior Quantitative Developer to design, build, test, and deploy systematic trading strategies. The ideal candidate combines strong software engineering skills with quantitative finance and algorithmic trading experience. Key Responsibilities Develop and maintain quantitative trading strategies. Build backtesting, optimization, and portfolio construction frameworks. Implement walk-forward analysis, Monte Carlo testing, and robustness validation. Work with market data including equities, futures, forex, and crypto. Integrate machine learning models into trading workflows. Collaborate with Java execution and platform engineering teams. Deploy research into production trading environments. Monitor strategy performance and improve risk-adjusted returns. Success Metrics Build 100+ validated strategy candidates annually. Develop production-grade research infrastructure. Improve portfolio Sharpe ratio and reduce drawdowns. Create scalable AI-driven trading models. Requirements 5+ years Python development experience. Strong knowledge of Pandas, Polars, NumPy. Experience with VectorBT, Backtrader, or QuantConnect LEAN. Strong statistics and quantitative finance knowledge. Portfolio optimization and risk management experience. Experience with futures, forex, equities, or crypto trading. Knowledge of PostgreSQL, TimescaleDB, and cloud environments. Git, Docker, CI/CD experience. Preferred Skills Machine learning (XGBoost, LightGBM, PyTorch). FIX protocol knowledge. Interactive Brokers, Alpaca, or LMAX integrations. Java or C++ exposure. Experience with institutional trading systems. Benefits Competitive salary. Performance bonus. Stock options/equity consideration. Opportunity to work on a global AI trading platform.

Location: New York, NY or Remote About Us: EvolutionIQ’s mission is to deliver state of the art technology that helps insurance claims teams make claims handling more accurate, fair, and efficient, so that more people impacted by injury or illness can continue their lives with dignity and stability. We are currently experiencing massive growth and to accomplish our goals, we are hiring world-class talent who want to help build and scale internally, and transform the insurance space. Our team is our #1 priority, and we have been named one of Inc.’s Best Workplaces 3 years in a row and Built In’s Best Places to work in 2025 and 2026! About You : A Senior Machine Learning Engineer who thrives on first principles problems that no one has ever worked on before. You love innovation and making an immediate impact, and are constantly driving toward launch and delivery thanks to your high degree of ownership and control. You love working on new and challenging problems, not incremental or marginal improvements, and you're supercharged by having interesting, unsolved problems continually present themselves. In this Role, You Will: Build ML models for production use Perform in-depth exploratory data analysis to scope ML opportunities, identify potential issues in the data, and lay the groundwork for model design, training & evaluation strategies Suggest and implement new features to improve model performance and business logic Write and review production-quality PRs daily Translate client business problems into ML problems Build scalable machine learning models for claim time series forecasting and NLP text understanding Continue to grow and learn in the data & ML space with a focus on business problems that require making sense of large-scale structured and unstructured datasets Requirements : 2+ years of experience as a Machine Learning Engineer or Data Scientist, preferably in highly scalable production environments. You have deep exposure to data science & ML tech stacks (sklearn, Keras / PyTorch, tuning frameworks), data analysis & modeling, feel at home in Python/Pandas/Jupyter, and strive for scalable technical concepts and solutions. You have excellent written and verbal communication skills and can effectively correspond between technical and non-technical audiences. You’re a team player who is solutions-oriented. Experience leveraging Claude Code and related tools is a strong plus Even Better if You Have the following: Knowledge of Dagster, BigQuery, Vertex AI, GCP, Terraform, Spark, Kubernetes Owned and deployed, and maintained model-serving pipelines in production Expert skills at writing clean, efficient, and easy-to-understand code with unit tests and functional design patterns Experience with advanced statistical modeling, especially probabilistic programming Ambitious, collaborative, and empathetic values A work style that is open to giving and receiving critical feedback and collaborating effectively across teams Work-life, Culture & Perks: Compensation: The base salary range is $190-225K, with flexibility depending on a candidate’s background and experience. An annual bonus plan and company equity plan (RSUs) are also included in our compensation package. Well-Being: Medical, dental, vision, short & long-term disability, life insurance and AD&D, and 401k matching. Additional family, wellness, and pet benefits. Home & Family: Paid time off and sick leave, 100% paid parental leave (16 weeks for primary caregivers and 12 weeks for secondary caregivers). We offer a flexible schedule for new parents returning to work. Office Life: Catered lunches, happy hours, pet-friendly spaces, and monthly technology stipend. Growth & Training: $1,000/year for each employee for professional development, as well opportunities for tuition reimbursement. Sponsorship: We are open to sponsoring candidates currently in the U.S. who need to transfer their active visa. Please check with our Recruiting team if your visa is applicable for transfer. EvolutionIQ appreciates your interest in our company as a place of employment. EvolutionIQ is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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 VC and adjacent tasks (controllable TTS, voice design 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. You will thrive in this role if you: - See research and engineering as two sides of the same coin and enjoy owning work end-to-end. - Are results-oriented, flexible, and willing to pick up whatever moves the needle. - Like collaborating closely with infra, data, and product to ship measurable improvements. - Enjoy designing experiments, listening tests, and metrics that correlate with user-perceived quality. - Eager to learn every-day, find and solve unique large-scale problems. 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. - 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. - 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 (>8B parameters, >500k hours of data). - Deep hands-on experience with diffusion and/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation. - Deep hands-on experience training audio VAEs, neural audio codecs, and vocoders latent/tokenizer design, reconstruction and perceptual objectives, adversarial training. - Strong experience with multi-node, multi-GPU distributed training (FSDP/DeepSpeed or equivalent). - 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 or multimodal generative models to production. - Background in working with large-scale ML data, and the ability to iterate on data and triangulate quality using both subjective and objective signals. - Experience with voice cloning, speech control/steerability, or expressive speech generation. - Notable publications and/or open-source contributions 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: Palo Alto Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE This role focuses on building and operating the end-to-end execution, training, and data infrastructure that powers Mistral’s agentic models and coding assistants. You will be a core contributor to our agent research stack: designing scalable systems for synthetic data generation, building ultra-fast training and RL execution environments, and maintaining high-throughput execution engines. You will tackle the engineering challenges at every step of the agent lifecycle: from orchestrating 1M+ concurrent and short-lived sandboxes for untrusted code execution to optimizing agent training codebases, distributed trajectory collection pipelines, and dataset processing workflows across massive hybrid and multi-cloud clusters. WHAT YOU WILL DO - Large-Scale Sandboxing Infrastructure: Design, deploy, and operate our high-throughput sandboxing platform, executing LLM-generated untrusted code across over 1 million isolated environments concurrently for model evaluation and interactive RL environments. - Agent Data Generation Pipelines: Architect and scale high-throughput pipelines for synthetic code generation, agent trajectories, rollouts, and self-play data collection to power post-training and RL loops. - Training Codebase & Systems Optimization: Optimize agent training codebases and distributed execution runtimes (PyTorch, Ray, SLURM/Kubernetes) to minimize multi-step rollout overhead, improve GPU utilization, and eliminate scaling bottlenecks. - Low-Latency Orchestration & Warm Pooling: Reduce sandbox cold-start times to sub-second levels using container warm pools, snapshot/restore technology (e.g., CRIU, microVMs), and optimized image delivery layers across hybrid clusters. - Multi-Cluster Queueing & Resource Allocation: Implement Kubernetes-native custom controllers, CRDs, and queuing systems to dynamically route short-lived evaluation, synthetic data, and agent execution tasks across diverse hardware fleets. - Isolation, Security & Security Boundary: Ensure strict multi-tenant network and process isolation for untrusted agent code using container/sandboxing runtimes (e.g., gVisor, Firecracker) and default-deny network postures. - Operational Excellence: Maintain high availability, telemetry, and automated self-healing across millions of transient jobs while participating in on-call rotations for critical agent training and execution pipelines. WHAT WE'RE LOOKING FOR - 4+ years of experience in Systems Engineering, Distributed Systems, Cloud Infrastructure, or MLOps supporting LLM/RL workloads. - Data & Pipeline Engineering: Proven experience building high-throughput data processing and generation pipelines for large-scale datasets (e.g., Ray, Spark, custom distributed queues). - Deep experience with Kubernetes & Container Tech: Strong expertise writing custom K8s operators/controllers, managing Linux cgroups/namespaces, and optimizing Docker image layers and distribution systems. - High-Performance Software Engineering: Advanced proficiency in Python, Go, C++ or Rust, with a track record of profiling and optimizing high-performance ML or backend systems codebases. - Sandboxing & Isolation Technologies: Hands-on experience with lightweight virtualization, container runtimes, or WASM (e.g., Docker, gVisor, Firecracker). - Queueing & Scheduling: Deep familiarity with task queue systems, resource schedulers, and low-latency queuing architectures for high-volume, short-lived workloads. - Comfort with Ambiguity: Passion for working directly alongside AI researchers to rapidly turn frontier agent ideas into scalable, production-grade infrastructure. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Krakow, Poland; Krosno, Poland; Lublin, Poland; Poland; Poznan, Poland; Warsaw, Poland Workplace: remote At TechBiz Global, we provide recruitment services to top clients from our international portfolio. We are currently looking for a Senior Data Scientist with strong hands-on experience in training AI models, particularly Large Language Models (LLMs) and Small Language Models (SLMs), using GPU infrastructure and real-world datasets . The ideal candidate should be based in Poland and able to clearly demonstrate their technical expertise, explain the tools and frameworks they use, and describe the complete model-training process—from data preparation to deployment and performance optimisation. Key Responsibilities Train, fine-tune, and optimise LLMs and SLMs using GPU infrastructure. Build and manage end-to-end machine learning training pipelines. Prepare, clean, structure, and process large volumes of real-world data. Select appropriate models, frameworks, tools, and training approaches based on project requirements. Apply techniques such as supervised fine-tuning, transfer learning, prompt tuning, and parameter-efficient fine-tuning. Monitor model performance and improve accuracy, speed, scalability, and resource utilisation. Work with structured, unstructured, time-series, telemetry, log, and streaming data. Clearly document and explain the tools, methods, and technical decisions used throughout the model-training process. Collaborate with engineering, data, and business teams to move models from experimentation into production. Troubleshoot issues related to model quality, training stability, GPU performance, and data pipelines. Proven professional experience as a Data Scientist, Machine Learning Engineer, AI Engineer, or similar role. Strong hands-on experience training or fine-tuning LLMs and/or SLMs. Practical experience using GPUs for AI model training. Strong Python programming skills. Experience with machine learning and deep-learning frameworks such as: PyTorch TensorFlow Hugging Face Transformers Experience with GPU-related technologies and environments, such as CUDA, distributed training, cloud GPU platforms, or GPU clusters. Strong understanding of model-training workflows, including data preparation, tokenisation, model selection, training, evaluation, and optimisation. Ability to clearly explain previous AI projects, tools used, technical challenges, and achieved results. Experience working with large and complex datasets. Good English communication skills. Based in Poland. Relevant Industry Experience Experience in one or more of the following industries or data environments would be highly valuable: E-commerce Finance or banking Insurance Healthcare or medical data Telemetry and IoT data Application or system logs Real-time and streaming data High-volume enterprise data environments Nice to Have Experience with distributed model training. Experience with LoRA, QLoRA, PEFT, quantisation, or model compression. Experience with MLOps tools and model deployment. Knowledge of Docker, Kubernetes, MLflow, or similar technologies. Experience using AWS, Azure, or Google Cloud for AI workloads. Experience deploying AI models into production environments. Knowledge of data privacy, security, and governance requirements.

Location: Palo Alto; New York Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE   Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions that turn the needle.   WHAT YOU WILL DO   • Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. You don’t panic when you see OOM errors or when NCCL feels like not wanting to talk. • Generate and curate data for pre-training and post-training, working on evaluations and making sure the model’s performance beats expectations. • Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment. • Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines. • Manage research projects and communications with client research teams.   WHAT WE'RE LOOKING FOR     • You have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences. • You’re an expert with PyTorch or JAX. • You’re not afraid of contributing to a big codebase and can find yourself around independently with little guidance. • You write clean, readable, high-performance, fault-tolerant Python code. • You don’t need roadmaps: you just do. You don’t need a manager: you just ship. • Low-ego, collaborative and eager to learn. • You have a track record of success through personal projects, professional projects or in academia.   NICE TO HAVE     • Hold a PhD / master in a relevant field (e.g., Mathematics, Physics, Machine Learning, Computer Science & Engineering), but if you’re an exceptional candidate from a different background, you should apply. • Can bring a variety of research experience (agents, multi-modality, robotics, diffusion, time-series). • Have contributed to a large codebase used by many (open source or in the industry). • Have a track record of publications in top academic journals or conferences. • Love improving existing code by fixing typing issues, adding tests and improving CI pipelines.     WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: United Kingdom, London Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Role Description: We are looking for an independent, dedicated Staff Machine Learning Engineer to join the Visual Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D. VCG’s mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency. What you’ll be doing: Develop and improve core generative models for graphics and video processing for a variety of applications, such as live video streaming, game rendering, etc. Research and prototyping of new methods for graphics or video enhancement, and data generation pipelines from generative models Collaborate with team members on drafting publications and patent submissions Participate and assist the team in core R&D work with other teams within Sony Interactive Entertainment What we’re looking for: PhD or MSc in Computer Science, Electronic Engineering, Artificial Intelligence, Machine Learning, Computer Graphics or a related field, or equivalent skills evidenced by work experience in the specific domain of the post 7+ years of experience in demonstrable research experience in industry or academia Strong background in: TensorFlow or PyTorch; Python and packages/libraries related to computer vision or graphics; evidenced by the development of advanced applications in image/video/graphics processing or computer vision (minimum 7 years), Evidence of leading technical developments by several publications in top-tier conferences like CVPR, ECCV, ICCV, SIGGRAPH or similar Solid background in one or more of the following: (i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design; (ii) design and test of advanced convolutional, recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner; (iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar Desired qualifications: Publications, e.g. in top-tier conferences and journals: IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, conferences like IEEE CVPR/ICCV/ECCV, NeurIPS, ICML, ICLR, or similar Some theoretical understanding of the graphics pipeline, including coordinate transformations between frames of reference, PBR materials, rasterization, lighting, and ray tracing Experience in image processing theory and methods, evidenced by the development of practical designs in this area Some experience with 3D engines such as Unreal or Unity; in particular, generation of datasets, extraction of G-buffers, motion vectors, etc. Benefits: Discretionary bonus opportunity Hybrid Working (within Flexmodes) Private Medical Insurance Dental Scheme 25 days holiday per year On Site Gym Subsidised Café Free soft drinks On site bar Access to cycle garage and showers Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Location: Paris; Amsterdam; London; Lausanne Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions that turn the needle. We are made of researchers, engineers, product builders, ex-founders who love tackling the most complex real world problems in the industry.   WHAT YOU WILL DO - Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. - Work across a diverse set of complex real world problems such as robotics, engineering, finance, cybersecurity, physics and more. - Generate and curate data for pre-training and post-training, working on evaluations and making sure the model's performance beats expectations. - Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment. - Collaborate with cross-functional teams to tackle complex use cases using agents and retrieval pipelines. - Manage research projects and communications with client research teams.   WHAT WE'RE LOOKING FOR - Fluent in English with excellent communication skills — at ease explaining complex technical concepts to both technical and non-technical audiences. - Expert with PyTorch or JAX. - Not afraid of contributing to a big codebase; finds their way around independently with little guidance. - Writes clean, readable, high-performance, fault-tolerant Python code. - Doesn’t need roadmaps: moves independently and navigates uncertainty with confidence. - Low-ego, collaborative and eager to learn. - Track record of success through personal projects, professional projects or academia. NICE TO HAVE - Ex-founder or builder - PhD / Master in a relevant field (Mathematics, Physics, Machine Learning) — exceptional candidates from other backgrounds welcome. - Research experience in agents, multimodal models, world models, robotics, diffusion models, cybersecurity, 3D understanding/generation, or time-series analysis. - Experience working on large code bases or large machine learning models at scale. - Contributions to a large open-source or industry codebase. - Publications in top academic journals or conferences. - Enjoys improving existing code: typing, tests, CI pipelines. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Serbia; Poland; Georgia; Cyprus; Portugal; Spain We are looking for a Senior ML Engineer / ML Lead to join the small, elite team of engineers and researchers building ML-driven trading strategies in the cryptocurrency markets. The system is currently live in production, showing strong results, and scaling rapidly. We are looking for an exceptional expert to take ownership of the product, evolve our architecture, and exponentially scale the profitability of our models. You will be working directly on real PnL. This position is designed for someone who wants to see the direct financial impact of their work, thrives in an environment of high autonomy, and is committed to writing high-performance code and building sophisticated mathematical models. Responsibilities • Develop and optimise the ML architecture for our trading strategies. • Drive the full research cycle: independently generate hypotheses, implement them into production, and monitor real-world performance/profitability. • Refine and improve existing ML models to boost effectiveness. • Identify and eliminate bottlenecks in data processing pipelines and execution strategies. Tech Stack • Python (primary language) • NumPy, numba, PyTorch / JAX Requirements • Experience in a Senior ML Engineer or ML Lead role. • Proven track record of developing profitable ML models or trading strategies. • Fundamental knowledge of mathematics, statistics, and probability theory. • Ability to operate in high-uncertainty environments: formulating your own hypotheses and validating them against data. • Experience in writing high-performance, production-ready code. Nice to Have • Experience in algorithmic trading or deep domain expertise in cryptocurrency markets. • Proficiency in C++ (specifically for reading and optimizing latency-critical code paths). • Strong academic background: competition accolades, Kaggle achievements, scientific publications, or graduation from a top-tier DS program (e.g., SHAD). • Interest in mentoring and team development. Benefits • Zero bureaucracy, high autonomy, and the chance to work alongside people who share a scientific approach and a commitment to high-quality engineering. • The opportunity to directly influence the product and see the results of your work reflected in real financial outcomes.

Location: Remote - Bengaluru, India MinIO is the data and memory foundation for enterprise AI. Built for the speed, scale, and economics that AI and analytics demand, AIStor and MemKV unify every layer of the data stack, from agentic and inference context memory to tables and objects across core, edge, and cloud. Trusted by 77% of the Fortune 100, MinIO is redefining how AI factories, intelligent applications, and autonomous agents secure, persist, and unlock the full value of their data. The Field Architect role at MinIO is a senior customer-facing technical leadership position focused on defining, validating, and guiding AIStor architectures for AI, analytics, cloud-native applications, and large-scale data infrastructure. You will serve as a trusted technical advisor to customers, partners, and internal teams, translating business and application requirements into robust technical designs that deliver measurable outcomes. You will collaborate closely with the broader MinIO team to ensure customers are successful through technical discovery, architecture design, proof-of-value execution, production onboarding, performance validation, and long-term expansion. Success in this role requires deep technical curiosity, hands-on architecture skills, strong communication, and the ability to connect AIStor’s capabilities to the data-intensive workloads that matter most to our customers. What You Will Do: Technical Leadership & Architecture Technical Architecture and Discovery: Lead technical discovery with customers and partners to understand their business goals, application requirements, data architecture, infrastructure constraints, and success criteria. Define scalable AIStor architectures that map directly to those requirements. AI and Analytics Workload Design: Define and validate AIStor architectures for AI/ML training and inference, analytics platforms, data lakehouse environments, high-performance data pipelines, large object workloads, and modern application data services. Object Storage and S3 Expertise: Serve as a subject matter expert in object storage, S3-compatible architectures, distributed systems, metadata, replication, erasure coding, security, performance tuning, and large-scale namespace design. Customer Engagement & Advisory Customer Technical Advisor: Build trusted relationships with customer technical stakeholders, including infrastructure, platform, analytics, AI, security, application, and operations teams. Guide customers toward informed architecture decisions and proactively identify opportunities to improve performance, resilience, scale, and operational efficiency. Proof-of-Value Execution: Lead technical evaluations, system configurations, performance testing, interoperability validation, and proof-of-value engagements that demonstrate AIStor’s value in real customer environments. Customer Success Beyond the Win: Partner with Customer Engineering, Product Management, and Engineering teams to help customers move from evaluation to production and from initial deployment to broader adoption across additional workloads, teams, and use cases. Ecosystem, Enablement & Field Operations Cloud-Native and Kubernetes Guidance: Provide technical leadership on Kubernetes, containers, automation, and cloud-native deployment patterns, including hybrid cloud, private cloud, public cloud, and edge infrastructure models. Solution Integration and Ecosystem Collaboration: Work with technology partners, reseller partners, and customer teams to support complete solutions involving analytics engines, AI platforms, Kubernetes distributions, data protection tools, identity systems, observability platforms, and public cloud services. Technical Presentations and Enablement: Build and deliver compelling architecture presentations, whiteboard sessions, demonstrations, best practices, workshops, and technical enablement sessions for customers, partners, and internal teams. RFP, RFI, and Technical Response Support: Contribute to or lead technical portions of RFPs, RFIs, RFQs, security reviews, architecture questionnaires, and competitive evaluations. Product Intelligence & Market Expertise Product and Engineering Feedback: Advocate for customer requirements internally by capturing field feedback, competitive insights, workload patterns, integration needs, and enhancement opportunities. Work with Product and Engineering teams to help inform roadmap priorities and improve customer outcomes. Competitive and Market Expertise: Stay current on object storage, data lakehouse, AI infrastructure, analytics, Kubernetes, cloud, and competitive storage trends. Help internal teams and customers understand where AIStor is differentiated. Your Skills and Experience: Strong background in enterprise storage, object storage, distributed systems, cloud infrastructure, data platforms, or high-performance data-intensive applications. Strong understanding of object storage concepts and S3-compatible architectures; experience with HTTP, REST APIs, JSON, security models, replication, data protection, and scale-out system design is highly valuable. 10+ years of experience in a senior customer-facing technical role such as Field Architect, Solutions Architect, Sales Engineer, Systems Engineer, or Technical Consultant. Hands-on experience with Linux, networking, storage systems, virtualization, containers, Kubernetes, and modern data center infrastructure. Experience designing or supporting AI, machine learning, analytics, data lakehouse, or large-scale data pipeline environments. Familiarity with ecosystem technologies such as Databricks, Starburst, Spark, Trino, Iceberg, Delta Lake, Kafka, PyTorch, TensorFlow, or other AI and analytics frameworks is a strong plus. Experience with public cloud and hybrid cloud environments, including AWS, Azure, GCP, OCI, or private cloud platforms. Ability to conduct technical discovery, design architectures, build demonstrations, guide proof-of-value execution, analyze performance, and communicate tradeoffs clearly. Comfortable working with heterogeneous enterprise environments involving compute, storage, networking, security, identity, observability, backup, disaster recovery, and application teams. Strong verbal, written, whiteboarding, and presentation skills, with the ability to communicate complex technical concepts to both technical and executive audiences. Ability to work collaboratively across customers, partners, Sales, Product, Engineering, and Customer Engineering teams. Strong customer-success orientation, intellectual curiosity, sense of ownership, and ability to prioritize effectively in a fast-moving environment. Willingness to travel as needed to support customers, partners, field events, and internal team activities. Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field; equivalent practical experience will also be considered. Equal Opportunity Policy (EEO) MinIO is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.

Workplace: fully We're looking for a Machine Learning Engineer to help build and improve AI-powered products. You'll work with large datasets, train and evaluate machine learning models, and collaborate with software engineers to bring models into production. This is a great opportunity for someone who enjoys solving practical problems with machine learning and wants to work on real-world AI applications. What you'll do Develop, train, and evaluate machine learning models. Prepare and analyze datasets for model training. Improve model performance through experimentation and testing. Deploy and maintain ML models in production environments. Collaborate with software engineers to integrate ML solutions into products. Monitor model performance and continuously improve accuracy. Stay up to date with new machine learning techniques and technologies. What we're looking for Basic understanding of machine learning concepts and algorithms. Experience with Python. Familiarity with machine learning libraries such as PyTorch, TensorFlow, or scikit-learn. Comfortable working with data and writing clean, maintainable code. Familiarity with Git. Good problem-solving skills. Strong written and verbal English communication. Nice to have Experience with large language models (LLMs). Experience with cloud platforms such as AWS, GCP, or Azure. Familiarity with SQL. Experience deploying ML models. Personal projects or open-source contributions related to AI or machine learning. Requirements 1+ year of experience in machine learning, software engineering, data science, or a related technical role or strong personal projects demonstrating ML skills. Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field is a plus, but not required. Passion for learning and building AI solutions. What we offer Fully remote work. Flexible working environment. Opportunity to work on real AI products. Challenging technical problems with room to grow. Collaborative and fast-moving team. Competitive compensation based on experience.

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 looking for an ML Engineer to own the production inference stack for our text-to-speech (TTS) and speech recognition (ASR) models, end to end. Our research team delivers trained model checkpoints; you will productionize them as low-latency, real-time streaming services that run cost-efficiently on GPUs. The role covers the inference engines, the Kubernetes infrastructure they run on, and the performance benchmarking and accuracy validation that let us ship changes safely. You will bridge research and production, and own latency, throughput, cost and accuracy as we scale. WHAT YOU’LL DO: - Build and optimize streaming inference engines for TTS and ASR models, including multi-stage model pipelines. - Optimize latency (time to first chunk, p50/p99) and throughput using continuous batching, CUDA Graphs, torch.compile and mixed precision. - Maintain numerical parity with the reference implementation through automated accuracy regression tests, per stage and end to end. - Deploy and operate GPU inference services on Kubernetes: autoscaling, containerization and infrastructure as code. - Build CI/CD for model artifacts, with model versioning and reproducible releases. - Own observability and performance benchmarking: p50/p99 latency, real-time factor (RTF), GPU utilization and cost per request. - Partner with research to productionize new models. WHAT YOU’LL BRING: - Hands-on experience deploying, scaling and optimizing ML models in production, beyond consuming model APIs. - Working knowledge of the full inference stack, from CUDA kernels to serving frameworks, with depth in some layers; expertise across all of them is not expected. - GPU performance optimization: profiling (Nsight Systems, PyTorch Profiler), memory bandwidth, CPU-GPU synchronization. - Solid understanding of model serving: continuous batching, KV cache management, request scheduling. - Production experience with Kubernetes, CI/CD and cloud infrastructure. - Strong Python and PyTorch, and a rigorous approach to performance benchmarking. - Experience with speech or audio models (TTS, ASR, voice conversion). - Experience with inference frameworks such as vLLM (vLLM-Omni), SGLang, TensorRT-LLM, TensorRT or Triton Inference Server. 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: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a VP of Research (Machine Learning) to define and lead the research direction behind its AI platform. This leadership role will shape how AI systems reason, learn, evaluate, and continuously improve within a product designed for high-frequency, real-world usage. Working closely with product and engineering leadership, this position will drive the long-term intelligence strategy, balancing cutting-edge research with practical production impact. Key Responsibilities Define and evolve the research roadmap for the platform's core AI intelligence, including context representation, memory, reasoning, planning, and orchestration. Evaluate and determine when to develop proprietary model architectures versus leveraging or adapting frontier open-source and commercial AI models. Design evaluation frameworks that measure real-world performance, robustness, safety, and long-term system behavior beyond traditional benchmark metrics. Lead the company's AI alignment, safety, and guardrail strategy as a core component of product development. Drive research and experimentation across advanced machine learning techniques, including: Retrieval-Augmented Training (RAG) Mixture of Experts (MoE) Model Distillation Multi-Agent Orchestration Multimodal AI Systems Partner closely with product and engineering teams to define and execute the intelligence strategy for AI-powered applications. Establish and maintain high standards for research quality, technical judgment, and engineering excellence across the organization. Requirements Extensive experience designing, building, and deploying machine learning systems in production environments. Strong technical expertise in model behavior, failure analysis, system evaluation, and long-term AI performance. Proven ability to translate advanced research into reliable, production-ready AI solutions. Demonstrated experience making high-impact technical decisions in fast-moving and ambiguous environments. Deep understanding of AI evaluation methodologies, model robustness, safety, and system reliability. Strong ownership mindset with the ability to lead technical strategy and execution. Passion for building practical AI systems that deliver measurable real-world impact rather than focusing solely on academic research or benchmark performance. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based model training and inference systems

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Technical Lead, Machine Learning to lead the execution of its AI platform by translating research into scalable, production-ready machine learning systems. This role sits at the intersection of research, infrastructure, and product, with responsibility for ensuring models are trainable, deployable, observable, and optimized for real-world performance. Working closely with research, engineering, and product teams, this position will drive the development of robust ML infrastructure while balancing performance, reliability, latency, and cost. Key Responsibilities Lead the end-to-end execution of machine learning systems, including data pipelines, training workflows, evaluation frameworks, inference architecture, and production deployment. Fine-tune and optimize models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation . Design, build, and operate scalable inference systems with a focus on latency, cost efficiency, and reliability. Develop and maintain data pipelines for both synthetic and real-world training datasets. Build evaluation frameworks to measure model performance, robustness, safety, and bias in collaboration with research teams. Optimize production deployments through GPU utilization, memory efficiency, inference optimization, and scaling strategies. Partner closely with application engineering teams to integrate machine learning systems into backend, desktop, and mobile products. Continuously improve production systems through rapid iteration, monitoring, and data-driven optimization. Requirements Proven experience building and deploying production-grade machine learning systems used by real users. Strong expertise working with large language models and understanding model behavior, limitations, and failure modes. Experience developing scalable ML infrastructure, training pipelines, and inference systems. Strong software engineering skills with the ability to write maintainable, production-quality code. Experience balancing real-world production constraints, including latency, reliability, scalability, cost, and safety. Strong ownership mindset with the ability to independently drive technical initiatives from design through deployment. Excellent communication and collaboration skills, with experience working in cross-functional, high-performing engineering teams. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based model training and inference systems

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Senior Machine Learning Engineer to design and build production-grade machine learning systems that power its AI platform. This is a hands-on technical role focused on solving complex machine learning challenges, translating research into scalable solutions, and delivering reliable AI systems that operate effectively in real-world environments. Working closely with research, product, and engineering teams, this position will independently own critical machine learning components from development through production deployment. Key Responsibilities Build and develop core machine learning systems that power AI-driven product capabilities. Own the end-to-end machine learning lifecycle, including data preparation, model training, evaluation, inference, deployment, and continuous iteration. Translate research concepts into reliable, production-ready machine learning solutions. Investigate and resolve model performance issues and production system failures using real-world operational insights. Continuously improve machine learning systems through rapid experimentation, measurement, and iteration. Collaborate closely with research, product, and engineering teams to deliver impactful AI features. Provide technical guidance through code reviews, best practices, and mentorship of fellow machine learning engineers. Develop solutions that balance real-world production requirements, including latency, scalability, cost efficiency, reliability, and safety. Requirements Proven experience building and deploying production machine learning systems used by real users. Strong understanding of modern machine learning models, including their behavior, limitations, and failure modes in production environments. Excellent software engineering skills with the ability to develop robust, maintainable, production-quality systems. Experience designing and implementing scalable machine learning pipelines and infrastructure. Strong ownership mindset with the ability to independently solve complex technical challenges from concept through deployment. Excellent problem-solving, communication, and collaboration skills within cross-functional engineering teams. Demonstrated ability to iterate quickly and continuously improve machine learning systems based on production feedback. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based training and inference systems