Machine Learning

Location: Munich (Hybrid/On-site depending on team setup) Type: Full-time Company Overview We are working with a fast-growing Vision / AI company building production software for the food and retail industry. Their systems help customers reduce food waste and improve operational efficiency - supporting sustainability goals through real-time computer vision and automation. With teams across Europe, the US, and Asia, we combine startup pace with real-world deployments at enterprise customers.The Role We are hiring a hands-on engineer to support the delivery of our computer vision / ML products into production. This role sits at the intersection of software engineering applied machine learning, with a strong focus on making ML models run fast, reliably, and at scale on edge devices. You will be responsible for our core video processing framework and deployment stack, working closely with senior ML engineers to ensure model inference performance, stability, monitoring, and field success. While you won’t be expected to design new ML algorithms or lead model training, you will be involved in diagnosing model issues in the field and improving real-world performance through optimization and iteration.This is a great fit for someone who enjoys real-world ML delivery: video streams, edge devices, inference performance, and production debugging.Key Responsibilities ML Model Runtime & Edge PerformanceMake ML models run efficiently on edge devices (latency, throughput, CPU/GPU utilization, memory constraints)Support inference optimization and troubleshooting (profiling, batching, pipeline tuning, runtime constraints)Investigate real-world model failures (data quality, camera placement, lighting, drift, edge-case behaviour) and work with ML engineers on mitigation strategiesEnsure robust model rollout processes: versioning, validation, safe deployment cyclesVideo Pipeline Engineering (Core Focus)Design and optimize real-time video processing pipelines using GStreamerIntegrate and manage streams from IP cameras (RTSP/ONVIF) and USB camerasDebug complex video stream issues (encoding/decoding, dropped frames, jitter, latency, network instability)Deployment & Production OperationsPackage and deploy services using Docker/Podman on Linux-based edge systemsTroubleshoot issues directly on production/staging Linux hosts (logs, profiling, system-level debugging)Implement and maintain monitoring and device health checks (e.g., Checkmk or similar)Event Streaming & InterfacesBuild interfaces between edge devices and online tools / connected machinesWork with event streaming systems (Kafka or similar) for detections, events, and telemetrydeep Kafka expertise isn’t required, but strong conceptual understanding isMust-Have Skills2–5 years of professional experience in software engineering / applied ML engineeringStrong Python skills (asyncio, threading, multiprocessing)Strong Linux skills: CLI, systemd, bash scripting, networking fundamentalsSolid experience with containerization (Docker or Podman)Comfortable debugging real systems remotely and working end-to-end (not just coding isolated modules)Interest in ML delivery and computer vision systems in productionNice to HaveExperience with GStreamer (big plus)Familiarity with computer vision pipelines (OpenCV, image processing)Experience with FFmpeg, RTSP, H.264/H.265, ONVIFWebRTC exposure (low-latency streaming)Kafka / message broker familiarityGerman language skills (corporate language is English)Why This Role is InterestingYou’ll work at the “real ML” layer: getting models running in production environments where conditions are messyStrong collaboration with senior ML engineers, with room to grow into more ML responsibility over timeDirect ownership of the edge inference video stack powering real customer deploymentsInternational team, low bureaucracy, hands-on culture

Job Title: ML Tech Lead – Multimodal AILocation: London / Remote Europe ConsideredCompensation: Competitive Base Salary Bonus Travel AllowanceAbout the RoleWe are seeking a hands-on ML Tech Lead to build and lead a brand-new team in a recently created, well-funded AI initiative. You’ll be responsible for shaping the direction of a cutting-edge platform for AI-driven video search and discovery, combining audio, video, and text data. This is a high-visibility role with the chance to impact creative teams and artists globally.Key ResponsibilitiesLead a multidisciplinary team of backend, frontend, and AI engineersArchitect and develop a multimodal AI search platform (video, audio, text)Design and build scalable content ingestion, indexing, and retrieval systemsIntegrate ML models into production search infrastructure (vector search, Elasticsearch/OpenSearch)Mentor engineers and foster a high-impact, collaborative team environmentDeliver robust, production-ready systems on modern cloud infrastructureWhat We’re Looking ForStrong experience in machine learning, especially multimodal modelsHands-on technical expertise in building large-scale search or recommendation systemsProficiency in cloud-based architectures and scalable production systemsLeadership experience: building, mentoring, and guiding engineering teamsPassion for music, media, or creative technology is a plusWhy This Role is ExcitingLead a newly created, high-impact initiative within a global entertainment leaderWork with massive audiovisual datasets and state-of-the-art AI technologyShape tools that directly support artists, creative teams, and content discoveryBe part of a well-funded, forward-thinking AI lab with long-term growth opportunities

Senior Agentic AI Engineer$300,000 - $400,000Onsite, Palo Alto (Remote for exceptional talent)Full time / PermanentA well-known, frontier GenAI company is undergoing a major product pivot, moving from single-modal generative experiences toward a consumer multi-agent ecosystem designed to feel genuinely autonomous, useful, and alive.They’re building the core infrastructure that will define how millions of users interact with AI agents daily. From planning and execution to memory, creativity, and proactive behaviour. This role sits at the heart of this shift: designing and shipping the systems that make intelligent agents function for 1M users.What You’ll DoDesign and evolve the agent runtime, the core loop handling reasoning, tool use, planning, memory retrieval, and response generationBuild agent capabilities across modalities (e.g. image/video generation, voice interaction, browsing, code execution) and ship themOwn LLM orchestration and model routing across multiple providers, optimising latency, cost, reliability, and qualityImplement memory systems that allow agents to learn from interactions (long-term memory, episodic recall, semantic retrieval)Prototype and productionize autonomous behaviours such as proactive task execution, scheduling, and goal-directed workflowsCreate evaluation frameworks and metrics that measure agent quality, personality consistency, and real user impactWhat “Great” Looks LikeYou’ve personally built and shipped agentic systems, not just prompt wrappers or demosYou’re comfortable owning ambiguous, greenfield problems and turning ideas into working product fastYou think in systems: distributed workflows, multi-step reasoning, orchestration, reliabilityYou code daily and care deeply about performance, UX feel, and real-world usefulness(If you’re looking for a narrowly scoped role, heavy process, or pure research track, then this won’t be the right fit.)Why JoinJoin at a genuine product inflection point, early access launch, new architecture direction, and strong internal momentumWork in a small, elite engineering cohort where each senior hire has outsized ownership and influenceHelp define the company’s next-generation agent platform and model infrastructure from the ground upCollaborate closely with product leadership and shape how consumer AI agents evolve in the real worldClear trajectory toward technical leadership and founding-level impact as the organisation scalesIf you’ve built real agent systems and want to work on problems that don’t have playbooks yet, please apply with your resume!

A leading mobile ad platform is looking for a Data Science Manager to join its Programmatic Data Science team. This team builds algorithms that compete in real-time ad auctions, outsmarting industry giants and optimizing ad delivery across thousands of apps. The role combines leadership and hands-on work. As a manager you will grow and mentor a team of data scientists, guide technical strategy, and contribute directly to building machine learning solutions, including recommender systems and neural networks. Ideal candidates have 5 years in data science, 2 years leading teams, strong Python skills, and experience working with large-scale data (AWS, Kafka, Spark, Flink, S3, MySQL).Experience with MLOps is a plus.The role requires someone who can dive deep into technical challenges while communicating clearly across teams.Company offers a hybrid setup, flexible hours, relocation support to Hamburg, 30 vacation days, an in-house gym, mental health support, and regular team events. The office has central location and lake views, modern equipment, and a culture that values collaboration and celebrating success. This is an opportunity to lead a high-performing team, tackle cutting-edge challenges, and shape the future of mobile advertising.

Senior Machine Learning Infra Engineer | San Francisco | Competitive Salary EquityOur client is an early-stage AI company building foundation models for physics to enable end-to-end industrial automation, from simulation and design through optimization, validation, and production. They are assembling a small, elite, founder-led team focused on shipping real systems into production, backed by world-class investors and technical advisors. They are hiring a Machine Learning Cloud Infrastructure Engineer to own the full ML infrastructure stack behind physics-based foundation models. Working directly with the CEO and founding team, you will build, scale, and operate production-grade ML systems used by real customers. What you will doOwn distributed training and fine-tuning infrastructure across multi-GPU and multi-node clustersDesign and operate low-latency, highly reliable inference and model serving systemsBuild secure fine-tuning pipelines allowing customers to adapt models to their data and workflowsDeliver deployments across cloud and on-prem environments, including enterprise and air-gapped setupsDesign data pipelines for large-scale simulation and CFD datasetsImplement observability, monitoring, and debugging across training, serving, and data pipelinesWork directly with customers on deployment, integration, and scaling challengesMove quickly from prototype to production infrastructure What our client is looking for3 years building and scaling ML infrastructure for training, fine-tuning, serving, or deploymentStrong experience with AWS, GCP, or AzureHands-on expertise with Kubernetes, Docker, and infrastructure-as-codeExperience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, or RayProven experience building production-grade inference systemsStrong Python skills and deep understanding of the end-to-end ML lifecycleHigh execution velocity, strong debugging instincts, and comfort operating in ambiguity Nice to haveBackground in physics, simulation, or computer-aided engineering softwareExperience deploying ML systems into enterprise or regulated environmentsFoundation model fine-tuning infrastructure experienceGPU performance optimization experience (CUDA, Triton, etc.)Large-scale ML data engineering and validation pipelinesExperience at high-growth AI startups or leading AI research labsCustomer-facing or forward-deployed engineering experienceOpen-source contributions to ML infrastructure This role suits someone who earns respect through hands-on technical contribution, thrives in intense, execution-driven environments, values deep focused work, and takes full ownership of outcomes. The company offers ownership of core infrastructure, direct collaboration with the CEO and founding team, work on high-impact AI and physics problems, competitive compensation with meaningful equity, an in-person-first culture five days a week, strong benefits, daily meals, stipends, and immigration support.