Senior Applied Scientist, Operations Research

Wolt

ABOUT WOLT At Wolt, we create technology that brings joy, simplicity and earnings to the neighborhoods of the world. In 2014 we started with delivery of restaurant food. Now we’re building the delivery of (almost) everything and you’ll find us in over 500 cities in 30 countries around the world. In 2022 we joined forces with DoorDash and together we keep on dreaming big and expanding across the globe. Working at Wolt isn’t always easy, but it’s definitely exciting. Here you’ll learn more, build more, and ship more than in most other companies. You’ll be challenged a lot, but also have a lot of fun on the way. So, if you’re a self-starter with drive and entrepreneurial spirit, this could be the ride of your life. Wolt is part of DoorDash — together we form one of the world's largest local commerce platforms, operating across DoorDash, Deliveroo, and Wolt markets in 40+ countries. Our Consumer organisation sits at the intersection of machine learning and customer experience, responsible for helping millions of customers every day find the right restaurants, dishes, and items in search, personalised recommendations, and discovery surfaces that feel intuitive and relevant. It's a domain where scale is truly global, the engineering and applied science problems are genuinely hard and scientifically interesting, and the impact on the customer experience and our business metrics is immediate and measurable. We’re looking for an Applied Scientist to join our Consumer org. In this role you’ll work on some of the most technically challenging ML problems across DoorDash: understanding what our customers are looking for, identifying key concepts in their queries and surfacing the most relevant results. You'll be embedded in a cross-disciplinary team of engineers, ML engineers and applied scientists with full ownership from research to production. If your expertise matches our domain and you want to work on hard problems at global scale alongside exceptional colleagues, we'd love to meet you. WHAT YOU’LL BE DOING As an Applied Scientist in Consumer, you’ll advance the ML models and methods that power how customers across DoorDash, Deliveroo, and Wolt find and discover content. You’ll work end-to-end collaborating closely with engineers and product managers to deliver real impact. DAY-TO-DAY IN THIS ROLE YOU’LL: * Design and develop ML models for search relevance, query understanding, and ranking that operate across DoorDash, Deliveroo, and Wolt’s 40+ markets. * Bring state-of-the-art solutions to our stack for the delight of our customers, helping the team to impact business metrics. * Work end-to-end on ML problems: from problem framing and data analysis through model development, offline evaluation, and production monitoring. * Collaborate with Software Engineers, ML Engineers, Product Managers, and Analysts to translate research insights into real customer impact. * Contribute to our group-wide Applied Science community through knowledge sharing, technical reviews, and raising the bar on ML practices. OUR HUMBLE EXPECTATIONS*  * You have 4+ years of hands-on experience in applied ML with a track record of shipping models to production (a PhD in ML with applied research experience is equally welcome). * You have solid experience in Search: query understanding, query intent prediction, or semantic search. * You are proficient in Python and experienced with ML frameworks and large-scale data processing. * You communicate complex technical ideas clearly and collaborate effectively with cross-functional teams. * Experience with NLP, dense retrieval, learning-to-rank, or embedding-based methods is a strong plus. WHAT WE OFFER In this role, you will have a direct and measurable impact on millions of customers every day. You’ll join a team of world-class applied scientists and engineers in DoorDash, Deliveroo and  Wolt who care about both rigour and craft, tackling problems that are genuinely hard and impactful in ways that few companies can offer. Together with your lead, you will have the opportunity to create a personalised development plan to grow your strengths and to develop new capabilities. 📍 This role can be based in one of our tech hubs in Berlin, Helsinki, or Stockholm, or you can work remotely anywhere in Finland, Sweden, Germany. We also offer relocation support to help you join us. NEXT STEPS* Once you apply, you will go through 4 steps in our hiring pipeline: * TA Screen: a 30-minute introductory call with one of our Talent Partners to learn about your background and tell you more about the role. * Hiring Manager interview: a conversation covering your domain experience in Search or Personalisation, past projects, AI fluency, and ways of working. We will tell you more about the team, use cases, our tech stack, etc. and it will be your opportunity to ask questions. * Coding & System Design: a technical session focused on ML system design and problem-solving with some of the team’s ML Engineers. * Project / Expertise Deep Dive: an interview with the team’s Applied Scientists, going deep on your domain expertise and past projects. OUR COMMITMENT TO DIVERSITY AND INCLUSION We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

Doctolib

WHAT WE DO At Doctolib, we are building AI-powered healthcare solutions that make a real difference in the lives of millions of patients and healthcare professionals every day. Our AI organization is at the heart of this mission, developing cutting-edge voice and language technologies that reduce administrative burden for doctors and improve patient care. The Voice team sits within the Applied AI organization and powers three flagship AI products: * Consultation Assistant - automatically transcribes and structures doctor-patient conversations to feed downstream NLP components (medical facts extraction, summarization) * Clinical Dictation - real-time streaming dictation with medical vocabulary boosting and LLM-based post-processing * Phone Assistant - AI-powered telephone assistance for medical practices, combining near-real-time ASR with conversational AI Our stack includes state-of-the-art models (FastConformerCTC, Whisper, NeMo), modern inference infrastructure (Triton, Ray Serve, MLFlow), and spans multiple languages (French, German, and beyond). YOUR ROLE We are looking for a Senior Engineering Manager to lead our Voice teams. This is a high-impact leadership role that sits at the intersection of applied ML research, engineering delivery, and people management. You will directly manage 2 teams (8 engineers) working across multiple ASR product perimeters. You will be the promoter of technical decisions for your scope and a key advocate for your teams across the organization.   WHAT YOU WILL DO People Management * Lead, coach, and develop a team of 8 ML engineers and data scientists across 2 teams * Own performance management, career development, and talent retention for all direct reports * Drive a culture of high standards and fast execution * Act as Hiring Manager: partner with Talent & People to attract and recruit top talent Technical Ownership * Own the technical direction of your perimeter, including architecture choices and trade-offs * Maintain full visibility across all technical domains covered by your teams, with no blind spots * Deeply understand system design and architectural constraints to challenge and guide your teams effectively * Ability to deeply understand architectures, challenge technical decisions, assess trade-offs, and ensure your teams are building the right things the right way Strategy & Delivery * Define and drive the roadmap for your teams in alignment with organizational OKRs * Navigate ambiguity and make fast, informed decisions in a constantly evolving scope * Identify synergies across teams and ensure technical coherence across the ASR perimeter * Contribute to broader Applied AI initiatives as a senior engineering leader WHAT WE ARE LOOKING FOR Must have * 3+ years of people management experience leading ML or Data Science engineering teams * Strong technical background in Machine Learning (classical ML is a must) * Ability to understand, challenge, and make architectural decisions on complex ML systems (at a level benchmarked against leading industry standards) * Deep understanding of system design, trade-offs, and technical risk management * Experience thriving in a startup-like environment: fast decisions, ambiguity, frequent scope changes * Versatile profile, comfortable operating across multiple technical domains simultaneously * Fluent in English; French is a plus Nice to have * Hands-on experience with ASR, speech processing, or audio ML * Familiarity with LLMs and their integration into production ML systems * Experience managing multi-team organizations or acting as a manager of managers WHAT WE OFFER * Join our mission to improve access to healthcare across the world and have a meaningful impact on millions of people every day * A decisive leadership role with real ownership from day one * The opportunity to work on cutting-edge AI at the intersection of voice, LLM, and medical technology * A strong engineering culture with high standards and a bias for impact * Continuous development opportunities: learning programs, knowledge sharing, internal mobility * Full remote flexibility * Competitive compensation and benefits package THE INTERVIEW PROCESS 1. Recruiter Call (30 min) 2. Hiring Manager Interview - in-depth discussion on people management approach and leadership experience 3. System Design Interview (SDI) - assess architectural thinking and ability to challenge complex ML systems 4. Behavioral Interview (BHV) - assess leadership, decision-making in ambiguity, and management philosophy 5. Offer

Vestiaire Collective

Vestiaire Collective is the leading global online marketplace for desirable pre-loved fashion. Our mission is to transform the fashion industry for a more sustainable future by empowering our community to promote the circular fashion movement. Vestiaire was founded in 2009 and is headquartered in Paris with offices in London, Berlin, New York, Singapore, Ho Chi Minh, and warehouses in Tourcoing (France), Crawley (UK), Hong Kong and New York. We currently have a diverse global team of 600 employees representing more than 50 nationalities. Our values are Activism, Transparency, Dedication and Greatness and Collective. About the Role We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective. While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace. You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies. Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact. What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code. You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication, immediately improving our trust and safety ecosystem. Mid-Term Foundation (MLOps Lifecycle & Infrastructure): Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring. You will scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations), design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience. Long-Term Vision (Centralizing 360-Degree MLE Capabilities): Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization. You will transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms. Who You Are Must-Haves: Experience: 5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems. Production Infrastructure: Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow). Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure. MLOps & Pipelines: Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles). You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases. Feature Stores: Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment. Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued. Strategic Builder Mindset: You are an analytical builder who thinks long-term. You can successfully evaluate TCO for bespoke internal systems versus enterprise tools, anticipate technical liabilities, and design robust architectures that handle unpredictable peak traffic surges. Collaboration & Engineering Hygiene: Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution. Nice-to-Haves: Relevant Domain Expertise: Background in E-commerce, Single-SKU Marketplaces, Search & Recommendation, Trust & Safety, or Counterfeit Detection. Vision, Edge & Optimization: Hands-on experience with Vector Databases, Visual RAG pipelines, deploying Deep Learning VLM models, and optimizing models for edge computing or low-latency inference (e.g., ONNX, TensorRT). Infrastructure & Observability: Advanced experience with containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), and data transformation workflows (dbt). Familiarity with setting up advanced monitoring for model performance, concept drift, and system health (Datadog, Prometheus).