Engineering Manager Stockholm, Sweden73,000–93,000 SEK per month benefits Hybrid – 3 days office / 2 days remote Full-time Most ML leadership jobs pull you away from the models. This one puts you in charge of them. You will lead the generative audio systems that create music and sound effects for a global content platform used by millions of creators. The models already exist. The research direction is clear. What is needed now is someone who can own the entire system and push it into production at scale. You will guide how large diffusion models for music are trained, evaluated and deployed. Your decisions determine how these models evolve technically and how they run in real products where latency, stability and cost matter. What you will build You will help build systems that automatically adapt music to video, generate sound effects directly from visual input, and allow creators to produce soundtracks in seconds. A small team of five PhD educated ML engineers and a contractor will rely on your technical direction while you shape how the technology moves from experimentation into production. You will work across the full machine learning lifecycle. Training large generative models. Defining evaluation strategies. Making architectural decisions about inference, optimisation and deployment. Working closely with platform and MLOps engineers to ensure the systems run reliably in production.  Why this environment is different The models are trained on a proprietary catalogue of licensed music and structured datasets created through a global network of artists who produce and remix tracks specifically for training. This produces a dataset most AI labs simply do not have. You will also work close to the research frontier, with collaborations involving groups connected to unicorn start up labs and tier 1 universities.  The result is rare: frontier generative model work inside a stable, profitable company where the technology actually ships to users.  What you bringDeep experience training large machine learning models. Experience with generative models such as diffusion, audio models, vision models or large language models. Strong ML system design skills across training, evaluation and production deployment. Comfort guiding engineers and making architectural decisions that shape how ML systems evolve. Experience shipping ML systems where latency, reliability and cost matter. Team and setup You will lead a team of five PhD educated engineers and one contractor working on generative audio systems. The team works closely with platform engineering, data infrastructure and MLOps to ensure models move from experimentation into production features.  Curious? If you have trained large generative models before and want ownership of the entire system rather than a narrow piece of it, this will likely be interesting. Send a message / apply, and I can share more context.

AI Product Manager - GenAII’m currently working with an AI native company that recently raised €3M and is building a series of vertical AI agents designed to automate real operational workflows across multiple industries.They already have a strong engineering team and are now looking for a Product Manager who can sit very close to engineering and help drive the pace of building and shipping AI products.This is not a traditional roadmap focused PM role. The person coming in will be heavily involved in hands on product development around AI agents, working with engineers on prompting, prototyping, defining agent behaviour, and helping run evaluation workflows to improve model performance.They are looking for someone comfortable operating in an early stage AI environment, where product ideas move quickly from concept to prototype to shipped product. The PM will also spend time speaking with customers to understand workflows and help design how these AI agents can solve real operational problems.The company operates more like an AI lab, identifying opportunities for automation, building AI agents around them, and then taking the successful products to market.If you are currently building AI driven products or exploring agent based systems, it could be a genuinely interesting conversation.

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 Research Engineer – Generative AIGermany - Remote first €80,000 – €100,000 2 year contract  This role sits inside a research-driven engineering team building real Generative AI systems that are meant to leave the lab and prove their value in the world.It is about building working GenAI agents, putting them in front of partners, stress testing them, improving them and demonstrating that they solve meaningful problems. The domains range from public safety and social services to finance. The common thread is impact. In the first six months, you would join an applied project where the goal is to prototype a GenAI agent and convince an external partner that it creates tangible value. You would work closely with a senior researcher, iterating quickly, shipping regular merge requests, refining features, spotting technical risks early and improving the system week by week. There is a strong emphasis on being able to explain what you built, both to technical peers and to non-technical stakeholders. The environment is intentionally exploratory. New models, new agent frameworks, new tooling. If something promising appears, you are encouraged to test it. The team meets in person every Tuesday in Heidelberg, but beyond that there is flexibility. English is the working language.You might be refining prompts and evaluation loops for LLM-based systems, experimenting with coding agents, shaping system architectures, or mapping out a lightweight roadmap for how a prototype could evolve into something commercial. You will be close to decision making, not buried in a narrow implementation silo.Who we're looking for:Working with LLMs or GenAI in practice since at least 2023, comfortable building in Python with proper version control.A Master’s or PhD in Computer Science, AI or a related field fits well.Industry experience matters more than labels.Experience with coding agents such as Cursor or Codex is particularly interesting, as is familiarity with modern GenAI libraries and lightweight MLOps tooling.Just as important is adaptability. The technology moves fast and so does the direction of applied projects. The interview process is technical but practical. There is an initial technical conversation focused on engineering and GenAI fundamentals, followed by a motivational discussion, and then an in-person day that includes collaborative coding using AI coding agents. The coding session focuses more on how you think and structure a solution than on perfect syntax. This is suited to someone who enjoys building at the edge of what is currently possible with Generative AI, but who also cares whether the result genuinely improves something for real users.If this sounds interesting, please apply here and a member of the team will be in touch.

Senior ML Engineer – Autonomous Driving (Mapless, AI-First) A well-funded European deep-tech company is building fully AI-driven, mapless autonomous driving technology in collaboration with leading OEMs and Tier 1 suppliers. We are hiring experienced ML engineers who want to move beyond incremental ADAS and work on large-scale, AI-native autonomy systems deployed directly on vehicles. What You’ll Work OnLearning-based scene understanding from raw multimodal sensor dataOnline road topology & lane connectivity extractionMultimodal transformers / graph neural networks for dynamic traffic modelingEnd-to-end perception → prediction → planning architecturesEnsuring geometric & temporal consistency in real-world drivingDeployment of production-grade ML models to embedded vehicle systemsThis is not simulation-only research. Models are trained at scale and validated directly on real vehicles. What We’re Looking ForStrong ML fundamentals (deep learning, transformers, large-scale training)Solid Python skills; C for production integrationExperience in one or more of:Autonomous drivingRobotics3D computer visionMultimodal learningSensor fusionLearning-based planningPhD is welcome but not required. Real-world deployment experience is highly valued. Why Join?Flat technical structure with real ownershipStrong compute infrastructureClose collaboration with major automotive partnersEquity / stock optionsOpportunity to shape next-generation autonomy from the ground upLocation: Germany (hybrid model available)

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.

Senior MLOps / ML Infrastructure Engineer About the Company Our client is a Series B, venture-backed deep-tech company building a Physics AI platform that helps engineering teams bring products to market faster, reduce development risk, and explore better designs with greater confidence. The platform combines large-scale simulation data with modern machine learning to generate high-fidelity predictions of physical behavior in near real time. Customers include leading organizations across aerospace, automotive, and advanced manufacturing, working on some of the most demanding real-world engineering problems. The Role This role focuses on building and operating the infrastructure that powers physics-based AI systems at scale. The position enables ML engineers and scientists to train, track, deploy, and monitor models reliably without managing low-level infrastructure. The work sits at the intersection of ML systems, cloud infrastructure, and large-scale simulation data, with a strong emphasis on performance, reliability, and developer productivity. It is a hands-on engineering role in a fast-moving, in-office environment, working closely with ML researchers, platform engineers, and product teams. What You’ll DoDesign, build, and maintain robust MLOps infrastructure supporting the full ML lifecycle, from experimentation and training through to production deployment and monitoringImplement automated training pipelines, experiment tracking, and model lifecycle management using tools such as Kubeflow, MLflow, and Argo WorkflowsDevelop scalable data pipelines capable of handling large volumes of unstructured data, particularly 3D geometric data and physics simulation outputsDeploy machine learning models into production inference systems with strong standards for performance, reliability, and observabilityManage model registries and integrate them with CI/CD workflows to support consistent and reliable model releasesImplement monitoring systems that continuously track model health and performance in productionCollaborate closely with ML researchers, platform engineers, and product teams to evolve the infrastructure platform for physics-based AI applicationsWrite production-grade code and optimize cloud infrastructure, primarily on Google Cloud Platform, while making thoughtful trade-offs around scalability, cost, and operational simplicity using Docker and KubernetesWhat We’re Looking ForBachelor’s degree or higher in Computer Science, Data Science, Applied Mathematics, or a closely related field5 years of industry experience building MLOps platforms or ML systems in production environmentsStrong proficiency in Python, with working knowledge of BASH and SQLHands-on experience with cloud infrastructure such as GCP, AWS, or AzureExperience with containerization and orchestration tools including Docker and KubernetesFamiliarity with modern MLOps frameworks such as Kubeflow, MLflow, and Argo WorkflowsExperience building and maintaining scalable data pipelines, ideally working with unstructured or high-dimensional dataAbility to independently deploy models and implement monitored inference systems in productionComfortable troubleshooting complex distributed systems and building reliable infrastructure that other teams depend onNice to HaveInterest in physics simulation, scientific computing, or HPC environmentsExperience building production MLOps platforms in deep-tech or simulation-heavy environmentsFamiliarity with additional programming languages such as Go or C Working Style and Culture This role suits someone who enjoys startup environments, learns quickly, and communicates clearly across disciplines. The team works on-site five days a week and values close collaboration, fast feedback loops, and hands-on problem solving. There is a strong belief that great infrastructure should be largely invisible, enabling engineers and scientists to move faster without friction.

Founding Machine Learning Research Engineer (Evaluation & Model Iteration Focus) Location: Bay Area Onsite We’re working with a pioneering stealth-stage company in the Bay Area that is redefining how AI is evaluated in healthcare.   Founded by ex-Stanford AI Lab researchers, ex-AWS, with deep expertise in representation learning and working on LLM interpretability.  We are looking for a Founding ML Engineer to: Lead investigations into model behavior, failure modes, and uncertaintyDeliver decision-grade evidence that informs FDA submissions and hospital adoptionWork directly with medical imaging vendors and hospitalsCombine hands-on ML skills with strong customer-facing judgment  To succeed in this role, we're looking for a genuine interest in rigorous evaluation/testing of ML systems, especially in medical AI.  This is a high-impact, high-ownership role, your work will directly influence real-world outcomes, FDA approvals, and how high-stakes AI is governed.  Compensation includes competitive salary $200k - $250k   meaningful early-stage equity (1–3%).  If this sounds like something you’d be excited about, please apply with your resume and we can set up a quick conversation to share more details.