Research Engineer - Speech and Audio Machine Learning

About the Institute of Foundation ModelsThe Institute of Foundation Models is a premier research laboratory committed to the development, comprehension, application, and risk management of foundation models. Our mission is to propel advancements in research, cultivate the next generation of AI innovators, and contribute significantly to a knowledge-driven economy.Joining our team provides a unique opportunity to engage with cutting-edge foundation model training alongside distinguished researchers, data scientists, and engineers. You will confront some of the most fundamental and impactful challenges in AI development while contributing to the creation of revolutionary AI solutions capable of transforming entire industries. Your strategic and innovative problem-solving abilities will be crucial in positioning MBZUAI as a global center for high-performance computing in deep learning, facilitating groundbreaking discoveries that will inspire future AI trailblazers.The RoleAs a Research Engineer focused on speech and audio machine learning, you will serve as the link between research and production constraints, ensuring competitive performance. You will oversee the complete performance lifecycle of our machine learning speech systems, from designing scalable training infrastructures to achieving ultra-low-latency inference capabilities.

About the Institute of Foundation ModelsWe are an innovative research laboratory dedicated to the development, understanding, and application of foundation models. Our mission is to propel research forward, cultivate the next generation of AI innovators, and contribute significantly to a knowledge-based economy.Joining our team means you will engage in the advanced training of state-of-the-art foundation models, collaborating with leading researchers, data scientists, and engineers. You will address some of the most pressing and transformative challenges in AI development, contributing to pioneering AI solutions with the potential to revolutionize entire industries. Your strategic and innovative problem-solving skills will be vital in positioning MBZUAI as a global leader in high-performance computing for deep learning, driving discoveries that will inspire future AI visionaries.The RoleAs a Research Scientist focusing on speech and audio machine learning, you will play a key role in designing and training cutting-edge end-to-end neural speech models. You will be tasked with developing crucial intellectual property, evolving beyond traditional ASR to TTS systems and creating native audio-to-audio multimodal architectures.

Join us as we seek to expand our team with an innovative Audio and Multimodal ML Engineer at connecthum, a thriving AI infrastructure startup dedicated to developing the safety and control framework for large-scale AI systems.About connecthum:An AI-native product company focused on enhancing AI safety and infrastructure.Supported by leading international investors.Expertly managing substantial AI traffic across diverse enterprise environments.Specializing in training and fine-tuning proprietary models for superior performance and reliability.A compact, highly skilled team that embraces a fast-paced development cycle.We are committed to creating a robust control and evaluation layer for AI systems, empowering organizations to define, test, and enforce AI behavior in real-world scenarios.Your Role:Train and enhance large-scale audio and multimodal models.Design and execute experiments focused on architecture, data mixtures, and training strategies.Develop and optimize audio data pipelines to ensure efficiency.Enhance inference speed, latency, and production readiness for models.Deploy models end-to-end in low-latency environments.Establish substantial evaluation metrics that extend beyond standard benchmark scores.Collaborate closely with research and engineering teams to drive innovation.This is a dynamic, hands-on position where research and production converge.Technical Environment:Utilizing PyTorch-based training pipelines.Engaging in large-scale distributed training techniques.Implementing speech and audio modeling architectures.Integrating multimodal models for comprehensive solutions.Optimizing models through quantization, distillation, and streaming inference.Overseeing production deployment and serving systems.(Full technical stack details will be shared during the interview process.)Qualifications:Minimum of 3 years of experience in training deep learning models, particularly in audio or speech domains.Strong expertise in distributed training frameworks.Deep understanding of audio signal processing fundamentals.Proven experience in deploying models to production, with a focus on latency and performance.Exceptional problem-solving skills and collaborative spirit.

Join nabla as a Senior Machine Learning Engineer specializing in Speech to Text technologies. You'll work at the forefront of AI, developing innovative solutions to enhance communication through advanced speech recognition. Collaborate with a talented team to push the boundaries of machine learning applications, focusing on real-world impact and user experience.

About Mistral AIAt Mistral AI, we harness the transformative power of artificial intelligence to streamline processes, save time, and boost creativity and learning. Our innovative technology seamlessly integrates into everyday professional environments, enhancing efficiency and productivity.We democratize artificial intelligence through a suite of high-performance, optimized, open-source models and solutions. Our extensive AI platform caters to both enterprise and individual needs, featuring Le Chat, La Plateforme, Mistral Code, and Mistral Compute - a groundbreaking collection that delivers state-of-the-art intelligence to users.Our dynamic and collaborative team is passionate about the potential of AI to revolutionize society. Comprising a diverse workforce spread across France, the USA, the UK, Germany, and Singapore, we thrive in competitive settings and are dedicated to fostering innovation. We pride ourselves on being creative, humble, and team-oriented.Join us in shaping the future of AI and making a significant impact in the field. Explore more about our culture at https://mistral.ai/careers.Mistral AI participates in the E-Verify programRole OverviewResearch Engineering Team IntroductionOur Research Engineering team encompasses both the Platform (shared infrastructure & clean code) and Embedded (integrated within research squads) divisions. Engineers have the flexibility to shift along the research-to-production spectrum based on evolving interests and requirements.As a Research Engineer focused on Machine Learning, you will be instrumental in building and optimizing large-scale learning systems that underpin our open-weight models. Collaborating closely with Research Scientists, you will have the opportunity to join either:- **Platform RE Team**: Enhance the foundational training frameworks, data pipelines, and cluster tools utilized by all teams; or- **Embedded RE Team**: Integrate directly within a research squad (such as Alignment, Pre-training, Multimodal, etc.) and transform innovative ideas into scalable, repeatable code.Location: Paris / London (hybrid) or remote within EU/UKKey Responsibilities• Facilitate researchers by managing the intricate aspects of large-scale ML pipelines and developing robust tools.• Bridge cutting-edge research with production: integrate model checkpoints, streamline evaluation processes, and expose APIs.• Conduct experiments utilizing the latest deep-learning methodologies (including sparsified 70B+ runs and distributed training on thousands of GPUs).• Design, implement, and benchmark ML algorithms, while producing clear, efficient code.

TLDR: We are seeking an experienced Audio and Multimodal Machine Learning Engineer to develop, train, and deploy cutting-edge speech, audio, and multimodal AI models for an advanced AI safety platform that handles over 100 million API calls each month.About UsWhite Circle is at the forefront of AI safety, dedicated to creating a reliable and optimized framework for AI systems. Our innovative platform is powered by simple natural-language policies that dictate the acceptable behaviors of AI models. We automate the testing, enforcement, and continuous enhancement of these policies to ensure they scale effectively.Backed by $11 million from leading investors, founders, and executives from organizations like OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, and Datadog.Processing over 100 million API calls monthly.We specialize in fine-tuning and training our own large language models to outperform both open-source and proprietary alternatives in speed and cost.Our team is small yet immensely focused. If you are eager to tackle complex challenges, quickly see your contributions in production, and shape the future of AI safety, we want you on our team.Your Responsibilities:Train and refine large-scale audio and multimodal models from scratch and using pretrained checkpoints.Design and execute experiments, including architectural modifications, data mixes, and training methodologies.Create and maintain audio data pipelines, transforming raw recordings into training-ready datasets.Optimize models for production environments, focusing on quantization, distillation, and streaming inference.Implement end-to-end model deployment, ensuring low-latency serving from research checkpoints.Collaborate with research teams to translate experimental concepts into deployable features.Establish key evaluation metrics and benchmarks that are crucial for product performance.Ideal Candidate:3+ years of experience in training large-scale deep learning models within audio, speech, or acoustic realms.Proficient in PyTorch and experienced in distributed training frameworks (such as DeepSpeed, FSDP, etc.).Familiar with audio/speech architectures like Audio Qwen, Whisper, HuBERT, or Conformer.Experience with multimodal architectures such as Audio Flamingo, Omni Qwen, etc.

Join Our Team as a Machine Learning EngineerAt Harmattan AI, we are pioneering the development of autonomous and scalable defense systems that make a meaningful impact. With our recent $200 million Series B funding, which has propelled our valuation to $1.4 billion, we are expanding our innovative teams to deliver essential systems to allied forces.Our core values include a commitment to technological advancement with real-world applications, a relentless pursuit of excellence, ambitious goal-setting, and tackling the most complex technical challenges. We thrive in an environment where high standards, accountability, and execution are paramount.Role OverviewAs a Machine Learning Engineer on our Foundational team based in Paris, you will be crucial in developing the cognitive capabilities of our tactical robots. Your primary responsibility will be to architect and scale large, multi-modal foundational models that derive robust battlefield representations through Self-Supervised Learning (SSL) from extensive unlabelled Electro-Optical (EO) and Infrared (IR) data. These foundational weights will empower our Edge AI team to create highly accurate models for tactical hardware.Key ResponsibilitiesArchitectural Design: Innovate neural network architectures (such as Vision Transformers) and loss functions (including Masked Autoencoders and Contrastive Learning) to effectively learn from both paired and unpaired EO and IR datasets.Training Infrastructure Management: Oversee and enhance training pipelines across multi-node GPU clusters, optimizing mixed-precision training and data loading processes.Evaluation of Representations: Establish metrics and linear-probing benchmarks to validate that the latent space captures meaningful semantic features prior to model distillation.Data Strategy Development: Assess current EO/IR data lakes and implement cross-attention mechanisms to integrate diverse sensor features.Collaboration: Work closely with Data Engineers on data ingestion pipelines and partner with the Edge AI team to ensure seamless transitions of high-performance models.

Join Our TeamAt Doctolib, we are on a mission to revolutionize healthcare access and enhance the quality of life for healthcare professionals. As a part of our commitment to responsible artificial intelligence, we are seeking a Master’s level intern to assess biases in a pediatric dialogue system by analyzing interactions between AI and patients.Collaborating with Doctolib's data scientists and researchers from LISN (Université Paris-Saclay), you will help ensure fairness and reliability in our AI health solutions. One of the key objectives of this internship is to create an annotated and anonymized dataset that will be published as open-source. This five-month internship will allow you to work on a project with significant societal impact, at the intersection of natural language processing, health, and AI ethics.Your responsibilities will include:Conducting a literature review on bias assessment methods in health-related language modelsAnalyzing a corpus of anonymized dialogues between the AI system and pediatric patients/parents to identify potential biasesDefining and implementing an annotation scheme to characterize identified biasesBuilding an evaluation framework based on demographic characteristic disaggregationApplying this framework to the dialogue system and comparing results with other autoregressive models

Cohere is seeking a research intern for the summer in Paris, with a focus on reinforcement learning. This position is intended for students or recent graduates eager to gain practical experience in artificial intelligence and machine learning research. Role overview Collaborate with Cohere's research team on reinforcement learning projects Take part in research activities and contribute to ongoing studies Analyze data and assist in developing algorithms to improve reinforcement learning methods Who this role is for Students or recent graduates with an interest in reinforcement learning Those looking for hands-on experience in AI and machine learning research This internship provides an opportunity to develop research skills and work with others in a collaborative environment.

At Lifen, we believe that medical data has the power to revolutionize the healthcare system, but only if it is accessible. More accessible data leads to reduced administrative burdens, improved care coordination, and faster scientific discoveries.Since 2015, our mission has been to unlock this potential. Our AI-driven solutions enable healthcare professionals to effortlessly share medical information and empower researchers to accelerate clinical discoveries, impacting millions of patients in France every day. As we prepare to expand across Europe, our success hinges on two key factors: our teams and our mission.Today, over 150 individuals work both onsite and remotely to unleash the potential of health data. We have connected 800 hospitals and 150,000 healthcare professionals, and we are just getting started. We seek individuals who embrace complexity, are eager to tackle challenging problems, and are ready to build the future of healthcare collaboration.Our AI TeamThe AI team at Lifen delivers over one million real-time predictions daily, powering our flagship product, Lifen Document, which transforms the exchange of medical reports among practitioners in France.With six years of experience in handling medical data, our team consisting of 5 ML Engineers and 1 Engineering Manager excels at building robust and secure models that drastically streamline healthcare professionals' administrative tasks.From layout extraction to pseudonymization, fine-tuning LLM for medical data extraction, and applying NLP algorithms to structure documents, we encompass the entire intelligent data processing chain in healthcare. What You'll Do with UsAt Lifen, Machine Learning addresses concrete challenges in processing medical documents, utilizing exploratory approaches in NLP and computer vision. During this internship, you will work on innovative topics at the intersection of research and industrial application. On a daily basis, you will:• Conduct experiments on NLP and vision challenges, exploring new approaches (calibration, confidence and context attribution in LLMs, reinforcement learning, multimodal models...).• Carry out thorough scientific monitoring and implement methods from recent publications, adapting them to our specific use cases in the medical field.• Rigorously evaluate the performance of your models...

Voodoo is looking for a Senior Machine Learning Engineer in Paris to design and implement machine learning algorithms for mobile games. This position centers on building technology that enhances the gaming experience for millions of players. Key responsibilities Develop and deploy machine learning models specifically for mobile gaming applications Work closely with product and engineering teams to bring algorithms into live products Experiment with new methods to improve gameplay and increase user engagement Working at Voodoo Voodoo is recognized for its creative approach to mobile gaming and commitment to innovation. The company values practical solutions that make a real difference in game development and player satisfaction.

About Yubo Yubo is a social discovery platform designed for authentic online connections. Unlike traditional social networks, Yubo removes likes and follows, encouraging genuine interaction, especially among Gen Z users. The platform supports millions worldwide, aiming to redefine how people connect online, both now and in the future. Safety sits at the core of Yubo’s mission. The team works closely with industry experts and NGOs to set high standards for child protection and digital well-being. This commitment shapes the product and the company culture, with a focus on user protection, support, and education. Role Overview: Staff Machine Learning Engineer Machine learning is central to Yubo’s growth and product evolution. As a Staff Machine Learning Engineer, you will work at the intersection of large-scale data and ambitious technical goals. The role involves tackling challenges across a global user base, with data spanning images, text, and real-time interactions. Key Areas of Impact Content moderation for images, text, and user behaviors Building and refining recommendation systems and user engagement strategies Developing behavioral detection and trust/safety models Exploring new use cases, such as dynamic pricing and growth optimization Yubo’s machine learning infrastructure is evolving. Some existing models are not yet fully integrated, and lifecycle management processes are still maturing. As the company increases its reliance on ML, there’s a growing need for reliable, well-structured systems that can scale with demand. Many ML applications remain unexplored, offering significant room for innovation. Establishing a scalable, dependable ML operating model will be key to unlocking this potential. Location Paris or full remote.

TLDR: We are seeking a skilled Machine Learning Engineer / MLOps Specialist to take ownership of our inference stack, optimizing serving engines and developing vector search pipelines. Your role will involve bridging the gap between Research and Product to deliver models that are efficient, cost-effective, and ready for production.About UsWhite Circle is an AI Safety company dedicated to enhancing the safety, reliability, and optimization of AI systems. Our platform is centered around defining straightforward, natural-language policies that dictate the appropriate and inappropriate behaviors of AI models. We continually test, enforce, and enhance these policies at scale to ensure optimal performance.We have successfully raised $11M from leading investors, founders, and executives from prominent organizations, including OpenAI, Anthropic, Hugging Face, Mistral, DeepMind, Datadog, and Sentry.Our infrastructure handles over 100 million API calls each month.We specialize in fine-tuning and training our own large language models (LLMs) to operate faster and more economically than any competing open or proprietary models.Your ResponsibilitiesOversee the end-to-end inference infrastructure: enhance latency, throughput, and cost efficiency across our model fleet.Develop and scale model serving solutions using TensorZero, vLLM/SGlang/TRT, and Kubernetes.Design and maintain vector search pipelines utilizing vector storage technologies.Demonstrate familiarity with support metrics (SLAs, FCR, deflection) and define service health KPIs.Transform research outcomes into practical applications: collaborate with the research team, assess production readiness, and manage deployment processes including formatting and sampling parameters.Your QualificationsA minimum of 3 years of experience in deploying high-performance ML systems in production, beyond just training notebooks.In-depth experience with inference optimization, including troubleshooting latency issues and understanding the differences between theoretical and actual throughput.Proficient across the tech stack: from CUDA kernels to Kubernetes manifests and Grafana dashboards.Preferred Qualifications: Experience with Rust, custom Triton kernels, and performance benchmarking.Why Join White Circle?Competitive salary ranging from $100,000 to $150,000 plus equity options.20 days of paid vacation annually.Opportunity to work from Paris with a hybrid work model, including a relocation package.Access to top-tier medical insurance in France.

Ubisoft Entertainment S.A. seeks a Team Lead Machine Learning Engineer in Paris to guide a group of engineers focused on enhancing gaming experiences through advanced machine learning techniques. This leadership role centers on both technical direction and team development within a creative environment. Key Responsibilities Lead a team of machine learning engineers, providing support and direction on project work. Foster collaboration and knowledge sharing among team members. Supervise the design, development, and deployment of machine learning solutions for gaming projects. Role Focus This position emphasizes both hands-on technical oversight and people management. The team works on projects that apply advanced machine learning to improve player experiences in Ubisoft games.

Your RoleAt Doctolib, we are dedicated to revolutionizing healthcare delivery through the innovative use of AI technologies.As a Senior/Staff Machine Learning Engineer, you will be pivotal in crafting, deploying, and enhancing the evaluation framework that guarantees our AI Health Companion operates safely, reliably, and supportively for millions of users, including both patients and healthcare professionals.Joining a dynamic, cross-disciplinary team of Machine Learning Engineers, Product Engineers, and Medical Experts, you will develop strong evaluation pipelines for agentic AI systems—models adept at reasoning, planning, and navigating intricate healthcare data.Your key responsibilities will encompass:Establishing and owning the evaluation strategy for our AI systems, including metrics, protocols, datasets, and tools.Creating and overseeing automated evaluation pipelines to track model quality, safety, and alignment through various iterations.Conducting systematic experiments to evaluate reasoning, factual accuracy, robustness, and overall user experience.Collaborating closely with model developers and research scientists to provide insights and foster continual improvement.Contributing to research and internal knowledge sharing regarding LLM evaluation methodologies and best practices.Our Technological LandscapeWe operate on a fully cloud-native platform that integrates web and mobile app interfaces, supports multiple languages, and is tailored to meet the specific requirements of various healthcare specialties. Our approach involves modularizing our platform to function within a distributed architecture through reusable components.Our technology stack includes Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.We prioritize ethical AI applications across our products to empower both patients and healthcare professionals. Explore our AI vision here!

Role Overview iliad-free is hiring a Machine Learning Engineer in Paris. This position focuses on developing and improving products with advanced machine learning techniques, including work with large language models (LLMs). The role sits within the tech and digital teams, where close collaboration drives new solutions for telecommunications challenges. What You Will Do Work alongside technical and digital colleagues to design, build, and refine machine learning models Apply modern algorithms and technologies to real-world product needs Contribute ideas and code that shape current and future projects Impact Results from this work help iliad-free stay competitive and innovative within the telecommunications sector. Contributions in this role have a direct effect on ongoing and upcoming projects.

Role overview sia is looking for a Machine Learning Engineer based in Paris. This position centers on building and refining machine learning models and algorithms that power sia’s software and platforms. The goal is to develop solutions that address practical, real-world needs. What you will do Design and develop machine learning models that support sia’s products Work closely with colleagues from various teams to address technical challenges Help enhance and evolve AI-driven features across our offerings Team and culture Join a group of professionals who value technology’s influence and enjoy working together on meaningful problems.

About the Role meilleurtaux is looking for a Machine Learning Operations Engineer to join the team in Paris. This role focuses on improving how machine learning models support business operations. What You Will Do Work closely with teams across the company to put machine learning models into production Ensure smooth integration of these models into daily business workflows Apply machine learning knowledge to strengthen operational processes Location This position is based in Paris.

Our Mission At Doctrine, we have been committed since 2016 to a significant democratic challenge: making law more accessible. We are the leading legal AI platform enabling lawyers and legal professionals to free themselves from repetitive and time-consuming tasks, allowing them to focus on what truly matters at every stage of their work—be it understanding a case, researching information, or drafting documents. Today, Doctrine attracts over 1 million visits monthly, with dozens of new legal professionals joining us each day. Our ambition extends beyond borders: we aim to establish ourselves as the European leader in legal AI. Following our successful launch in France and Italy, we are currently expanding into Germany with a strong belief that we must think globally while implementing locally. We prioritize understanding the unique needs of our clients and deeply comprehend the nuances of each legal system. And this is just the beginning!Our Values Challenge the Status Quo. We champion bold ideas and intelligent risk-taking.Liberty and Responsibility. We promote autonomy, individual impact, and ownership.Knowledge is Power. Information is at the heart of Doctrine's mission, and we strive for continuous learning.Release Early, Release Often and Listen to Your Customers. We believe in the power of iteration and the importance of constantly listening to our market, our clients, and their challenges.The ContextWe are seeking a Senior Machine Learning Engineer to assist us in building the first legal intelligence platform.You will join a high-impact squad, playing a central role in scaling the entire Engineering team at Doctrine.This position involves cross-functional missions across squads, countries, and multiple products (search engine, chatbot, recommendations… very engaging work).Our technical stack includes Python, PyTorch, Airflow, Gen AI, NLP, and LLM.

Join our dynamic team at iliad-free in Paris as a Machine Learning Engineer Intern. This is an exciting opportunity for students or recent graduates to gain hands-on experience in the world of machine learning and large language models (LLM).As an intern, you will work closely with our talented engineers to develop innovative solutions that leverage advanced machine learning techniques. You will have the chance to contribute to real projects and enhance your skills in a collaborative environment.