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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.

Your Role We are looking for a Quantum Design Engineer to help create and refine superconducting qubit architectures with built-in error resilience. The role focuses on maximizing coherence and fidelity while embedding error mitigation directly into circuit layouts and topology. You will take designs from simulation to fabrication and measurement, collaborating closely with fabrication and measurement teams to ensure theoretical advances translate into functional, scalable quantum devices. What You’ll DoDesign and simulate superconducting qubits and multi-qubit circuits, balancing high coherence with intrinsic error protection.Develop novel architectures that reduce reliance on external error correction through design-level solutions.Benchmark decoherence mechanisms and engineer circuit geometries to minimize loss, crosstalk, and noise sensitivity.Use electromagnetic and circuit simulation tools (e.g., HFSS, Sonnet, COMSOL, Qiskit Metal) alongside custom simulation workflows.Collaborate across disciplines to integrate design with fabrication and measurement protocols.Document and analyse results, using structured feedback loops to drive continuous improvement. Who You AreMSc or PhD in Physics, Electrical Engineering, or a related field.Expertise in superconducting qubits, circuit QED, or microwave quantum devices.Proficiency with electromagnetic and circuit simulation tools (HFSS, Sonnet, COMSOL, Qiskit Metal, or equivalent).Strong understanding of decoherence mechanisms and strategies to mitigate them through design.Experienced in connecting simulation results with experimental validation.Clear communicator and collaborator across design, fabrication, and measurement teams. What We OfferOpportunity to define and shape the architecture of next-generation superconducting quantum processors.Early-stage responsibility with direct influence on prototypes and roadmap.Collaborative, science-driven environment spanning design, fabrication, and experiment.Professional growth opportunities including mentoring, training, and leadership development.Competitive compensation and benefits, including relocation support

Your Role We are looking for a Quantum Design Engineer to help create and refine superconducting qubit architectures with built-in error resilience. The role focuses on maximizing coherence and fidelity while embedding error mitigation directly into circuit layouts and topology. You will take designs from simulation to fabrication and measurement, collaborating closely with fabrication and measurement teams to ensure theoretical advances translate into functional, scalable quantum devices. What You’ll DoDesign and simulate superconducting qubits and multi-qubit circuits, balancing high coherence with intrinsic error protection.Develop novel architectures that reduce reliance on external error correction through design-level solutions.Benchmark decoherence mechanisms and engineer circuit geometries to minimize loss, crosstalk, and noise sensitivity.Use electromagnetic and circuit simulation tools (e.g., HFSS, Sonnet, COMSOL, Qiskit Metal) alongside custom simulation workflows.Collaborate across disciplines to integrate design with fabrication and measurement protocols.Document and analyse results, using structured feedback loops to drive continuous improvement.Who You AreMSc or PhD in Physics, Electrical Engineering, or a related field.Expertise in superconducting qubits, circuit QED, or microwave quantum devices.Proficiency with electromagnetic and circuit simulation tools (HFSS, Sonnet, COMSOL, Qiskit Metal, or equivalent).Strong understanding of decoherence mechanisms and strategies to mitigate them through design.Experienced in connecting simulation results with experimental validation.Clear communicator and collaborator across design, fabrication, and measurement teams.What We OfferOpportunity to define and shape the architecture of next-generation superconducting quantum processors.Early-stage responsibility with direct influence on prototypes and roadmap.Collaborative, science-driven environment spanning design, fabrication, and experiment.Professional growth opportunities including mentoring, training, and leadership development.Competitive compensation and benefits, including relocation support.Based in Garching, Germany, at a hub of quantum research and technology.

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