Machine Learning

Speech Algorithm Engineer (Speech LLM / SpeechLLM)$150,000 - $250,000San Francisco, Hybrid 3x per week in officeFull time / PermanentAbout the Role This company is already profitable, growing fast, and used by over 1.5M professionals globally. Revenue is tracking at ~$250M in under three years. The product works and is highly marketable, the next step is making its speech system significantly more accurate across languages, industries, and real-world conversations. We’re hiring a speech algorithm engineer to improve speaker diarization and keyword recognition in productio. This is applied, high-impact work that ships. What You’ll DoImprove speaker diarization and multi-language speech recognition accuracy in real customer conversationsDesign and optimize hotword and terminology recognition systems for industry-specific use casesFine-tune and train large speech models on substantial audio datasetsBuild clear evaluation frameworks to measure keyword accuracy and speaker separation performanceCompare open-source and commercial ASR systems and push performance beyond themWork closely with product and engineering to deploy models into live systems used dailyWhat “Great” Looks LikeYou’ve trained or fine-tuned speech models on large-scale datasets (not small research-only projects)You understand how speech systems behave in noisy, real-world conditionsYou’ve improved measurable production metrics (accuracy, diarization quality, keyword recall)You can read research and turn it into working systemsYou take ownership when performance drops Notable: If your experience is limited to light experimentation or purely academic research without production exposure, this likely won’t be a fit. Why JoinProfitable company at ~$250M run rateHybrid San Francisco team building both hardware and AI systemsReal ownership and visibility, not one engineer in a large orgGlobal product scale and meaningful datasetsClear growth path toward senior technical leadership as the audio function expandsStrong data security and compliance standards, this is enterprise-grade infrastructure

MLOps Engineer Barcelona or San Sebastián, Hybrid Fixed-term contract until 30 June 2026€45,000-€55,000 Salary €3,000 sign-on bonus €500 per month retention bonus €2,000 relocation support EU work authorisation required Total bonus package available over the contract: up to €5,000 depending on start date. You join one of Europe’s most recognised deep-tech scale-ups. Backed by major global investors and strong EU support, they have built one of the most credible AI compression products in the market. This compression tool is already live with major enterprise clients. Now they need more engineers to help deploy, monitor and scale it properly.Why apply? You will work alongside highly technical quantum and AI engineers operating at a very high level. You will gain hands-on exposure to large-scale LLM deployment, distributed training and real-world cost optimisation. You will have a globally recognised deep-tech brand on your CV, working on AI efficiency at scale. That combination of compression, distributed systems and enterprise deployment opens doors across AI infrastructure, LLMOps and high-performance ML environments. You get flexible working hours. Start early, start late, structure your day how you want. Hybrid setup in Barcelona or San Sebastián. You get meaningful bonuses on top of base salary. What you’ll actually be doing Helping take compressed LLMs and get them deployed, monitored and running reliably for enterprise customers. Improving automation, reliability and cost efficiency across the ML lifecycle. Working closely with researchers and platform engineers to bridge research and production.What you'll needExperience running LLMs in production. Comfort working with the infrastructure around them, cloud, containers, CI/CD, Kubernetes, that sort of thing. Someone who understands what it takes to keep ML systems stable, monitored and efficient once they’re live. If you’ve touched production LLM systems and the infra that supports them, this is likely relevant.

Lead AI Engineer Fully Remote - Europe I am working with a European health tech scale up that is building production grade AI systems used by insurers, governments, hospitals, and pharma groups to support complex drug pricing and reimbursement decisions.They are hiring a Lead AI Engineer to own the AI architecture end to end and drive how LLM based systems are designed, evaluated, and shipped into production.The role is hands on and strategic. You would lead AI initiatives across RAG pipelines, agent workflows, and tool orchestration, while mentoring a small engineering team and working closely with product and platform leads. A big focus is on building systems that are observable, cost aware, and reliable, not demos.The environment suits someone who has spent time taking LLM systems from concept to real usage, has strong Python and backend experience, and understands tradeoffs around latency, throughput, and evaluation. Experience with LangChain or LangGraph, FastAPI style services, cloud infrastructure, and MLOps practices is important.The product domain sits at the intersection of healthcare, pricing, and access to medicines, with real world impact and complex constraints. The team is senior, international, and remote first across Europe.If this sounds aligned with the kind of problems you enjoy working on, feel free to share your updated resume/cv!

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

Location: Munich (Hybrid) Type: Full-time Company Overview We are working with a fast-growing Vision / AI software company building production-grade computer vision systems for the food and retail sector. Their products help customers reduce food waste, improve operational efficiency, and contribute to sustainability goals by enabling better decision-making through real-time visual intelligence. With an international footprint across Europe, the US, and Asia, they combine startup speed with real-world deployments at large enterprise customers. The Role We are looking for a Senior Machine Learning Engineer to take hands-on technical ownership of a key vision product that is moving into field testing with major retail partners in Germany. This role is ideal for someone who enjoys being deeply involved across the entire ML lifecycle - from model development and training through to deployment on edge devices at customer sites. You will act as a hands-on technical lead for the product, driving model improvements, performance validation, and production rollouts. Key Responsibilities Model DevelopmentDesign, implement, and iterate on deep learning architectures for real-time object tracking and event detectionTrain and optimize object detection models using production datasets and domain-specific video dataContinuously improve model robustness for real-world conditions (lighting changes, occlusions, camera angles, motion blur, etc.)Performance Evaluation & ValidationBuild and execute evaluation workflows for accuracy latency benchmarkingTest models using benchmark video datasets and dedicated hardware setupsMonitor model performance regressions and validate incremental updates before releaseDeployment & Integration (Edge / Production)Own the technical process of deploying model updates into production systemsEnsure stable integration of models into the wider software stack running on-siteSupport field testing cycles, troubleshooting and optimizing performance on edge devicesTooling & PipelinesMaintain and improve internal pipelines for:automated model trainingdata versioningperformance testingreproducible experimentationDrive best practices across model development and deployment workflowsRequirements5–8 years experience in Machine Learning / Deep Learning / Computer VisionStrong proficiency in Python PyTorchHands-on experience training object detection models (e.g., YOLO-style / Faster R-CNN / transformer-based detectors, etc.)Solid software engineering skills in a Linux environmentStrong ownership mindset: able to maintain and advance the full ML stack end-to-endMotivated to learn and apply new methods and improve production qualityMust HaveNative German speaker (customers and field partners are Germany-based)Nice to HaveExperience deploying ML models to edge devices / embedded environmentsFamiliarity with performance profiling / inference optimizationExperience with real-time video pipelines and production CV systemsWhat’s On OfferHybrid working model in MunichFlat hierarchies, high ownership, hands-on cultureInternational, multicultural environment with colleagues across multiple regionsDirect impact on a product entering real-world rollout with major German retailersBenefits/perks including mobility options, company events, and additional corporate benefits

Senior / Principal Research Scientist – Core AI Algorithms (Autonomous Systems)Location: Germany (Remote-first within Germany, on-site in Frankfurt every 2–4 weeks)About the RoleWe are partnering with a global automotive OEM building a core AI research and algorithm team responsible for the foundational intelligence behind next-generation automated driving systems.This role is research-driven and sits upstream of product teams. The focus is on inventing, validating, and transitioning new perception and world-modeling algorithms from research into production-ready systems. The team operates similarly to a big-tech research lab, but with a clear path to real-world deployment.Research Focus AreasDepending on background and interest, you may work on topics such as:3D scene understanding and world modelingOccupancy, motion forecasting, and dynamic scene reconstructionMulti-sensor perception (camera, LiDAR, radar)Representation learning for autonomous systems (BEV, implicit / generative 3D, Gaussian models, foundation models)Robustness, generalization, and long-tail perceptionLearning under weak, sparse, or noisy supervisionBridging offline training with real-world deployment constraintsKey ResponsibilitiesConduct original research in perception and autonomous systems with clear technical ownershipDesign and prototype novel algorithms and learning frameworksPublish at or contribute toward top-tier conferences and journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICRA, IROS)Translate research ideas into scalable, production-oriented implementationsCollaborate with applied ML, systems, and hardware teams to ensure feasibilityShape the long-term technical roadmap of the core AI organizationMentor junior researchers and engineers where appropriateRequired BackgroundPhD (or equivalent research experience) in Computer Vision, Machine Learning, Robotics, or a related fieldStrong publication record at top-tier conferences or journalsExperience conducting research within an industrial or applied settingExcellent understanding of modern deep learning methods and 3D perceptionStrong programming skills in Python and/or C Ability to work across the full spectrum from theory to implementationStrongly PreferredResearch experience in autonomous driving, robotics, or embodied AIWork on 3D perception, tracking, SLAM, or world modelsExperience at big-tech research labs, industrial AI labs, or advanced OEM R&DFamiliarity with real-world constraints such as runtime, memory, and system integrationPrior collaboration with product or engineering teamsWhat’s on OfferA research-first role with real influence on production systemsThe opportunity to define core algorithms, not just incremental improvementsA team culture that values publications, patents, and long-term thinkingRemote-first working model within Germany, with regular in-person collaboration in FrankfurtCompetitive compensation aligned with senior / principal research profilesWho This Role Is ForResearchers who want their work to ship into real vehiclesIndustry researchers seeking greater technical ownershipPhD-level candidates who enjoy both publishing and buildingProfiles combining academic depth with practical engineering maturityLooking forward to seeing your profile!

Hiring a DevOps Engineer for one of the most exciting deep-tech companies in Europe right now. This is a fixed-term role (until June 2026) based in Zaragoza or Barcelona, with a hybrid setup and real flexibility. You’d be joining a fast-scaling Series B company working at the intersection of quantum computing and AI. The tech is already live in production — compressing large language models by up to 95 percent and cutting inference costs by 50–80 percent. This isn’t theoretical. It’s being used by global enterprises today. Why this role is special You won’t be maintaining legacy systems. You’ll be building, scaling, and optimizing the infrastructure behind cutting-edge AI products used by Fortune 500 clients. You’ll have real ownership and a seat at the table when DevOps strategy decisions are made. What you’ll do • Optimize and scale high-performance inference and internal APIs • Build and improve CI/CD pipelines and automation • Own AWS infrastructure for secure, highly available systems • Work closely with engineers, product teams, and customers • Help deploy software directly into customer environments What we’re looking for • 2–5 years of DevOps experience • Strong AWS and infrastructure-as-code experience • CI/CD expertise (GitLab, GitHub Actions, Jenkins, etc.) • Docker and Kubernetes in production • Solid scripting or coding skills (Python, Bash, Go) • Someone who can troubleshoot fast and communicate clearly What’s on offer • Competitive salary • Signing bonus retention bonus • Hybrid working and flexible hours • Relocation support if needed • Equal pay guaranteed • International, high-impact environment