Machine Learning Engineer (Semantic Scene Understanding)

Harmattan AI

ABOUT US Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces. Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected. ABOUT THE ROLE As a Machine Learning Engineer on our Foundational team in Paris, you will build the "brain" of our tactical robots. You will design and scale large-scale, multi-modal foundational models that learn robust representations of the battlefield using Self-Supervised Learning (SSL) from massive amounts of unlabelled Electro-Optical (EO) and Infrared (IR) data. Your work provides the critical foundational weights that our Edge AI team distills into hyper-accurate models running on tactical hardware.   RESPONSIBILITIES * Multi-Modal SSL Architecture Design: Design neural network architectures (Vision Transformers) and loss functions (Masked Autoencoders, Contrastive Learning) to jointly learn from paired and unpaired EO and IR data. * Distributed Training Infrastructure: Manage and optimise training pipelines across multi-node GPU clusters, handling mixed-precision training and data loading. * Representation Evaluation: Develop metrics and linear-probing benchmarks to prove the latent space captures useful semantic features before distillation. * Data Strategy: Audit existing EO/IR data lakes and implement cross-attention mechanisms to fuse diverse sensor features. * Cross-Functional Collaboration: Sync with Data Engineers on ingestion pipelines and collaborate with the Edge AI team to ensure high-performance model handoffs. Candidate Requirements * Educational Background: A PhD or a highly research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics. * Proven Experience: Minimum of 5-6 years of experience for senior levels. Experience training and scaling deep learning vision models (ViTs, CNNs) from scratch in multi-GPU/multi-node environments. Successful application of novel SSL or multi-modal architectures (e.g., CLIP, MAE, DINO) to real-world, non-standard imaging data (IR, SAR, or hyperspectral). * Technical Proficiency: Hardcore PyTorch engineering skills combined with deep mathematical intuition for representation learning. Knowledge of system-level languages (C++, Rust, or Go) and resource optimisation for edge computing. * Complexity & Leadership: Ability to architect state machines for fault-tolerant data pipelines and mediate technical trade-offs between hardware and algorithm teams. * Commitment & Mindset: 100% dedication to Harmattan AI’s mission of providing an ethical defence edge to allied countries. A hybrid researcher-engineer mindset that treats data quality as seriously as algorithm design We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.

Datadog

Applied AI is where Datadog's ambitious AI bets get built and shipped (Bits Chat, updog). We sit at the intersection of research and product: turning promising capabilities from Datadog AI Research lab and the research community into production systems that reach real customers. The team builds specialized models that replace frontier models where they are not necessary, making AI capabilities faster, cheaper, and more secure. The mandate is to move fast from idea to customer impact, and when a product finds its footing, to set it up for growth.   As a Manager I in Applied AI, you will lead a team of engineers and applied scientists working on one of these challenges. You will define technical direction, run short feedback loops, make deliberate decisions about what to pursue or stop, and work closely with product managers, research teams, and cross-functional partners to ship AI capabilities that matter.   At Datadog, we place value in our office culture, the relationships and collaboration it builds and the creativity it brings. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.   What You'll Do  * Lead and develop a team of engineers and applied scientists focused on cost-efficient specialized models and AI security capabilities * Work closely with product managers, research teams, and cross-functional partners to shape the team's bets from initial framing through to broader adoption, with a clear definition of success criteria at each stage * Own end-to-end delivery of high-quality AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality * Navigate the unique challenges of shipping AI-powered products: balancing quality, latency, cost, and safety considerations. Drive evaluation and iteration practices for AI systems: define the quality bar and guide the team in building the offline and online evaluation pipelines needed to measure quality and detect drift * Contribute to cross-team collaboration and knowledge sharing across the broader AI organization * Support career growth for engineers through coaching, feedback, and fostering a culture of experimentation, innovation, and learning. Participate in hiring and help shape the future team as the organization grows Who You Are * A people-focused manager with experience leading and mentoring engineers, able to develop strong engineering talent in a fast-moving domain * A technical leader with deep expertise in one or more areas of AI or machine learning: large language models, retrieval-augmented generation (RAG), semantic search, agentic systems, deep learning, or NLP * Well-versed in evaluation methodologies for AI systems, both offline benchmarks and online metrics * A strong product instinct: able to anchor early-stage work in concrete customer problems, define success criteria before writing code, and actively contribute to shaping product direction alongside product and research partners * Experience taking AI products from 0 to 1 is strongly valued: able to bring structure to early-stage work by scoping clear hypotheses, moving quickly toward signal, and making deliberate decisions about what to pursue, pivot, or stop * BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field, or equivalent professional experience #LI-Hybrid ---------------------------------------------------------------------------------------------------------------------------------- About Datadog:  Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center. ---------------------------------------------------------------------------------------------------------------------------------- Equal Opportunity at Datadog: Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.  Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.  Privacy and AI Guidelines: Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.

Qonto

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here). Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ Join us as a Staff Machine Learning Engineer on our AI Product team to build and ship customer-facing AI for 600,000+ business customers. You'll combine Generative AI with proven machine-learning techniques to create products with measurable impact — adoption, faster task completion, user satisfaction — while ensuring reliability, privacy, and continuous monitoring in production. ➡️ What you'll do Develop ML models end-to-end: From understanding product requirements to training, evaluating, and deploying models in production. You design, iterate, and ship — not just prototype. Integrate ML into the product ecosystem: Align with Product Managers, Data Engineers, and Backend Engineers to ensure your models are seamlessly embedded in Qonto's financial services. Build the ML Ops framework: Create the infrastructure for the team to scale — model drift detection, performance tracking, automated retraining pipelines, monitoring, and alerts. Put models into production with rigour: Robust technical implementation, quality assurance, and continuous monitoring. Client-facing AI in financial services has no room for silent failures. Raise the bar for the team: Share best practices, contribute to internal tooling improvements, and mentor peers across the ML team. ➡️ What we're looking for 6+ years as an ML Engineer with ML Ops experience: You've developed and deployed client-facing ML products end-to-end — not internal tools or dashboards. You can show measurable impact on real users. Modelling expertise: Experience building and optimising machine learning models for external customers. You know when to use GenAI and when proven ML techniques are the better choice. Strong Python engineering: You write resilient, testable code at scale. Proficient with FastAPI (or similar), third-party service integration, and database interaction in production. ML Ops fluency: Familiar with tools that automate model retraining, performance checking, and drift detection. You've built or significantly improved ML infrastructure before. Fluent in English: Qonto's working language. ➡️ What we can offer you Customer-facing AI with real impact: Your models will be used directly by hundreds of thousands of business customers. You'll see adoption metrics, not just offline evaluations. A modern, flexible stack: Python, Snowflake, Kafka, Kibana, PostgreSQL, Airflow, AWS, Prometheus, ArgoCD, GitHub, Cursor. You have the freedom to test any tool as long as it helps reach the target. A team building AI at the core of fintech: 10 AI Engineers and 3 Data Ops working on innovative solutions at the heart of Qonto's financial services — not a side project. Clear IC growth track: Individual contributor career path for those who want to become deep experts in their field, with access to the latest AI technologies. ➡️ Your future manager Option A Your manager will be Marianne Borzic Ducournau, Head of Data Products. Her background? A graduate of École Polytechnique, Marianne went on to lead Data Science teams at Uber and Amazon in San Francisco before joining Qonto four years ago to build our Data Science team from scratch — hiring the founding members and defining the technical direction. What does she bring to the team? A rare combination of applied ML expertise and business context from Finance — she helps people see both the technical and the strategic side of what they're building. Option B Your manager will be Benjamin Wolter, Head of AI Products. His background? After earning his PhD in Physics and leading ML Engineering and Data Science teams across last-mile logistics and digital marketing, Benjamin joined Qonto to lead our AI Products team. What does he bring to the team? Deep technical ML expertise, practical experience building scalable ML systems, and a management style built around ownership and autonomy — he creates the conditions for people to grow without hand-holding.