AI/ML Engineer

Melotech

WHO WE ARE Melotech is revolutionizing media and entertainment. We create art through technology for humans to enjoy. In just 24 months, our work has been heard, watched and loved for over 3 billion minutes worldwide. Founded by entrepreneur and investor Soheil Mirpour, we are backed by top VCs Cherry Ventures, Speedinvest and GFC, alongside world-class angels from firms such as Spotify, Blackstone and KKR. WHAT YOU WILL DO As our ML Engineer Intern, you'll be the technical backbone powering our content platform. You'll tackle the critical questions: How do we build ML systems that scale to millions of users while maintaining low latency? What's the optimal architecture for training and deploying models that understand cultural trends in real-time? And how do we leverage cutting-edge models to enhance creative processes while preserving quality? Working fully autonomously alongside our founder and the team, your answers to these questions will directly influence our company's success. On a typical day, your tasks may include: * Building and deploying production ML models for within our content and product ecosystem * Designing scalable ML infrastructure and pipelines that handle massive media datasets * Implementing inference systems for content optimization across multiple verticals * Fine-tuning and deploying multimodal AI systems using MLOps best practices * Collaborating with data science teams to transition research models into production-ready systems * Optimizing model performance for cost efficiency while maintaining accuracy and speed requirements * Integrating ML capabilities into existing platforms and building APIs for seamless model consumption WHO YOU ARE You're a production-focused upcoming ML engineer who bridges the gap between cutting-edge tech and scalable systems. Your expertise lies in building robust ML infrastructure that powers real-world applications at scale. You thrive in fast-paced environments where your technical decisions directly impact business outcomes and user experiences. Typically, your profile will look like this: * Pursuing a degree in Computer Science, Machine Learning, Mathematics, Engineering, or related technical field * Hands-on ML engineering experience building production systems at Big Tech companies, high-growth startups, or media/entertainment platforms * Proficiency in Python, ML frameworks, and cloud platforms * Self-directed approach with ability to architect complex systems independently while collaborating across technical teams * You thrive in a fast-paced and performance-oriented environment * Colleagues would describe you as hard-working, ambitious and persistent * You're obsessed with music, video or social media WHAT MAKES THIS EXCITING You are one of the first employees of an ambitious team, changing the world of media and entertainment. Being early means every decision you make shapes our trajectory. You're not a cog in the machine but the captain of your own ship, rewarded for performance and respected for leadership. Flat hierarchies mean that your voice matters, your ideas get implemented, and your impact is immediate. We pay competitive salaries and make you an owner of the business with equity. We work remotely to give you complete freedom over your life, while meeting regularly around the world for global offsites where we strategize, bond, and push boundaries together. WHAT THE PROCESS WILL LOOK LIKE We hire on a rolling basis. Earliest starting date is always ASAP. Once you begin our process, you can progress from start to offer within a week, depending on how quickly you can move through each stage: 1. Take-home case study: Real-world project - showcase your skills and working style 2. Case interview: 90-minute case discussion - getting to know you & present and debate your results with a team member 3. Online assessment: Motivational questionnaire and aptitude test - are you made for the job? 4. Founder interview: 90-minute interview with our CEO - going deep on all topics 5. Team interview: Individual or group interview with other team members - depending on position 6. Offer, contract signing and onboarding Note: As we are still in stealth, you will learn more about Melotech as you progress through the stages. By the end of the Founder interview, you will have a full grasp of our business and the details of your role.

Awin

Purpose of position Data sits at the heart of the company. This role ensures that Awin can fully leverage the data available across the group, enabling the development of robust reporting capabilities that support stronger commercial decision making and elevate campaign management for client facing teams. As an AI/ML Engineer, you will be responsible for researching, designing, building, and automating predictive models, as well as establishing metrics to monitor model performance and accuracy. You bring strong communication and analytical skills, work effectively both independently and within a team, and contribute proactively to improving engineering standards and working practices. You possess a deep understanding of data and business requirements and can translate these into clear technical specifications. You write clean, maintainable, and high quality code, and you are capable of implementing a wide range of data extraction, transformation, and storage methodologies. Your expertise enables you to develop new predictive models while also enhancing and optimizing existing ones. In this role, you will break down complex requirements into structured, actionable tasks. As part of the Data Services department, you will collaborate with a global team of data engineers, data analysts, and other big data specialists across the organisation.   Key Tasks & responsibilities * Write clean, elegant and maintainable code with Data engineering and AI/ML best practices * Understanding business objectives and developing models that help to achieve them, along with the creation and monitoring of business relevant metrics * Find new ways solve complex business problems with self improving automated predictive models. * Evaluate existing models and recommend improvements for better result and performance efficiency * Develop best practices for building and orchestrating predictive models. * Be responsible for quality, accuracy and interpretation of the result sets.   Education & experience * Bachelor’s degree or higher in Data Science, Data Engineering or a related field, preferably with a strong focus on mathematics, statistics, or data engineering. * 2+ years experience as data engineer on AI/ML project with Python * Strong experience using Databricks, including Jobs, Asset Bundles, Delta Lake, and MLflow, as well as Azure data engineering tools such as Azure Data Factory and Azure Data Lake Storage (ADLS). * Solid understanding of Scrum practices and a strong Agile mindset. * Advanced proficiency in Python and its key data and ML libraries (NumPy, PySpark, Scikit learn, TensorFlow/PyTorch) * Working knowledge of generative AI models (e.g., ChatGPT, Claude), Databricks GenAI tooling (including embedding models), and modern data structures such as vector databases. * Strong expertise in designing end to end ETL solutions using Databricks, including identifying, extracting, and curating datasets for machine learning model development. * Strong knowledge of cloud platforms (Azure and AWS) and relevant services. * Hands on experience with big data technologies, including Databricks and Spark.   Skills & Core competences * Very strong analytical skills to translate business need into actions with proactive approach to task and challenges, delivering project on time and budget * Very strong understanding of the principles of machine learning * Create mind-set to bring new ideas and push the machine learning project to next level * Willing to change, learn and start initiatives * Ability to face existing challenges self-reliant with the courage to take autonomous decisions if needed * Very strong communication and interaction skills * Very strong project and process management competency, keen to deliver your project on time/budget without compromising on quality and results * Team player, willing to improve yourself   Our Offer * Flexi-Week and Work-Life Balance: We prioritise your mental health and wellbeing, offering you a flexible four-day Flexi-Week at full pay and with no reduction to your annual holiday allowance. We also offer a variety of different paid special leaves. * Remote Working Allowance: You will receive a monthly allowance to cover part of your running costs. In addition, we will support you in setting up your remote workspace appropriately. * Flexi-Office: We offer an international culture and flexibility through our Flexi-Office and hybrid/remote work possibilities to work across Awin regions * Meal Vouchers: You will be supported with a certain net sum to spend it on a variety of lunches. * Health & Wellbeing: The insurance covers several types of health, vision and / or dental treatments for you and for up to one additional family member. * Remote Working Furniture Package: After 3 months of employment, you will be eligible for a furniture package, which should enable you to set up a proper workplace at your remote working location * Appreciation: Thank and reward colleagues by sending them a voucher through our peer-to-peer program. Established in 2000, Awin is proud of our dynamic, social and inclusive culture. Like all businesses, we’ve had to adapt and nurture our culture in a virtual environment. Our virtual ‘Life @ Awin’ hub brings our colleagues from across the globe together for various social activities. Diversity & Inclusion are paramount to us, and we proudly pursue and hire diverse team members. We champion uniqueness and authenticity; this is who we are at our core. Our network of affiliate partnerships are diverse and transparent, as are the employees powering our vision to build the world’s leading open partner ecosystem. We welcome all backgrounds, identities, and experiences. If you need support at any point in the application or interview process, please let us know. Awin is part of the Axel Springer group. Learn more at axelspringer.com/en/, and explore the Axel Springer Essentials here: axelspringer.com/en/inside/the-essentials-what-we-have-adapted-and-why   Apply now to begin the next stage of your career at a progressive company that supports both your professional and personal development. #LI-RS1

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).