Machine Learning

Lead MLOps Engineer Zurich, Switzerland | Hybrid (1 day on-site per week) AI Consultancy CHF 130k – 136k Bonus Overview This is a hands-on MLOps / software engineering role within an AI consultancy that delivers production ML systems into enterprise environments. You’ll be embedded inside the pricing team of a large insurance company, working on an existing project where more engineering capacity is needed to get models into production and keep them running. The core problem is straightforward: models exist, now they need to be deployed, scaled, and made reliable on GCP. What you’ll be doing You own the path from trained model to production system. That includes building MLOps pipelines, deploying models into GCP, and ensuring they’re stable, monitored, and usable once live. You’ll work closely with data scientists on one side and stakeholders on the other to make sure what’s built actually runs and delivers value. Environment You’ll join a lean team: Head of AI Engineering one engineer. The consultancy model means you’re embedded with the client for long-term engagements (typically 1–2 years), so you stay close to the systems you build and continue improving them over time. You’ll work directly with both technical and non-technical stakeholders, with no layers in between. What this offers youOwnership of ML deployment into production on GCPWork on systems used day-to-day by the business (pricing decisions)Small team, high visibility, direct access to leadershipLong-term project continuity (1–2 years)Clear progression into Architect / Principal or consulting trackWhat you bring You’ve deployed ML systems into production and know where things tend to break. You’re strong on GCP, can operate independently, and are comfortable working directly with stakeholders. You care about system reliability, clean architecture, and making ML systems actually work in production. CompensationBase: CHF 130k – 136kBonus: typically ~1 month salaryCHF 2,500 annual learning budgetCHF 1,000 annual wellness budget

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!

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.

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.