Machine Learning Engineer (NLP) About the Company This early-stage environmental intelligence startup is building next-generation AI systems that help global organisations understand and plan for water-related risks. Their platform combines deep learning with physics-based modelling to generate high-resolution insights for some of the world’s largest infrastructure operators, consumer brands, and investors. Backed by leading scientific minds across climate, hydrology, and machine learning, the company is now expanding its capabilities by developing a new social risk function that captures the human, regulatory, and community dynamics that shape water outcomes around the world.Why JoinJoin a team pushing the boundaries of environmental intelligence, combining physical and social risk modelling into a unified AI platform. Work with world-class researchers, publish meaningful science, and help deliver tools with tangible global impact.Pioneer a new capability: You’ll be the first ML engineer dedicated to modelling social, political, and reputational water risk.Cutting-edge work: Blend NLP, LLMs, graph intelligence, and geospatial modelling into a real, production platform.Genuine impact: Your models will inform global water stewardship decisions across high-risk regions.Interdisciplinary collaboration: Work alongside scientists and researchers across climate, hydrology, and social systems.Early-stage ownership: Build from first principles in a fast-moving, mission-driven startup with strong early traction.What You’ll DoBuild NLP, LLM, and multi-modal pipelines to analyse community, regulatory, media, and public-sentiment signals — including stance detection, topic/event clustering, and stakeholder network mapping.Fuse unstructured social data with geospatial and physical-risk datasets to generate unified risk insights for real-world decision-making.Partner with climate and domain scientists to translate social signals into actionable risk metrics, contributing to both product development and peer-reviewed research.Deploy scalable, interpretable ML systems into production via APIs and platform infrastructure.What You Bring3 years building applied ML/NLP systems, ideally across text, geospatial, or social-network data, including sentiment/stance modelling and multi-source pipelines.Strong Python plus experience with PyTorch/TensorFlow, SQL, and modern LLM tooling (Hugging Face, LangChain, OpenAI APIs).Skilled with entity extraction, topic modelling, network/graph analysis, and data sourcing or weak supervision in multilingual environments.Passion for climate, water, or environmental risk, and comfortable working in an early-stage, collaborative, low-ego environment.Nice to HavePhD / Postdoc with track record of pace and quality of publicationsGraph ML experience or multi-modal fusion (text geospatial).LLM fine-tuning for domain-specific tasks.Deployment experience with FastAPI, Docker, or similar frameworks.Background or exposure to environmental science, hydrology, or social-data analysis.

AI Low-Code Architect (Contract, EU/CH) Location: Europe or Switzerland Workload: 100% Start: ASAP Seniority: Senior / PrincipalLength: 6 months Role Overview We are looking for a highly technical AI Low-Code Architect to support a large enterprise in evolving its automation and AI ecosystem. The position focuses on designing the platform setup, ensuring the right governance structures are in place, and guiding teams on how to build scalable AI and low-code solutions using Power Platform, Copilot Studio, and Azure AI. Key Responsibilities: • Define the technical setup for the organisation’s AI and low-code landscape, covering environments, integration points, and platform configuration. • Shape the security and identity model for the platform, ensuring safe access and consistent governance. • Set standards for how solutions should be designed, deployed, and maintained across Power Platform and Azure. • Lead technical working sessions with architecture, engineering, and security stakeholders. • Provide direction to development teams on how to structure apps, flows, connectors, and AI components. • Create clear architectural documentation to support delivery teams and future enhancements. • Advise on feasibility, technical risks, and overall solution approach for new initiatives. Required Experience • 6–10 years in cloud or Microsoft-focused architecture roles. • Deep understanding of Power Platform architecture (Dataverse, environments, solution structure, ALM). • Strong hands-on exposure to Copilot Studio, Azure OpenAI, Cognitive Search, Logic Apps, and Functions. • Solid grasp of enterprise identity, RBAC, and secure integration practices. • Fluent English. Nice to Have • Experience in large or regulated enterprise environments. • Knowledge of multi-agent AI patterns or Azure AI Foundry.

Project Manager – AI & Low-Code (Contract – 6 Months, Remote EU, Start ASAP) We’re looking for an experienced Project Manager to drive a Europe-wide AI & Low-Code program for a global enterprise client. This fully remote 6-month contract is ideal for someone who thrives on coordinating multiple teams, keeping projects on track, and delivering results in fast-paced environments. What you’ll do:Plan, track, and oversee delivery across multiple squads and countriesCoordinate developers, architects, secuirty and data teams Run agile ceremonies, manage dependencies, and resolve blockersProduce reports, dashboards, and updates for stakeholders and steering committeesSupport testing, go-live, and post-launch activitiesWhat we’re looking for:5–10 years’ experience managing IT or digital transformation projectsFamiliarity with conversational AI deliveryManaging distributed teams and international stakeholdersFluent in EnglishNice to have:PMP, Prince2, or Scrum certificationExperience with enterprise AI programs or Microsoft CoEDetails:Start: ASAPDuration: 6 monthsLocation: Remote (EU-based is a must)Rate: €400–500/day depending on experience

Role: Full Stack Robotics EngineerSalary: upto $250,000Location: San Francisco, CA Opportunity to work on next-generation AI-driven physical systems capable of general-purpose manipulation, experimentation, and manufacturing. I’m looking for several Full-Stack Robotics Engineer to architect, prototype, and harden high-precision electromechanical platforms. You’ll own subsystems end-to-end across motion planning, real-time control, sensing, actuation, mechanical design, and embedded firmware. This role is deeply hands-on and requires first-principles thinking, rapid iteration, and the ability to integrate across disciplines to deliver reliable, high-performance robotic capability. Responsibilities:Build and integrate motion planning, kinematics, control, and perception into robust robotic behaviors.Develop real-time control loops, actuator interfaces, embedded firmware, and system-level safety.Lead mechanical design of end-effectors, precision mechanisms, and structural components.Design electrical systems including sensing, power, actuation electronics, and data pathways.Prototype, validate, and iterate complete robotic stacks from fabrication to deployment.Work cross-functionally to align mechanical, electrical, firmware, and control architecture. Qualifications:Deep expertise in robotics, motion, and real-time control of complex electromechanical systems.Proven experience taking platforms from prototype to reliable operation.Strong mechanical, electrical, and firmware engineering skills.Builder mindset with a drive for quality and novel capability creation.

Role: Senior Digital Twin ML EngineerSalary: upto $250,000Location: San Francisco, CA Work on advanced AI-driven physical systems with broad manipulation and experimental capability. I’m seeking a Senior Digital Twin ML Engineer to build high-fidelity digital twins of robotic, electromechanical, and experimental platforms. You will design model-identification pipelines, calibration routines, dynamic-model learning systems, and multi-scale physics representations that support accurate predictive simulation and closed-loop interaction with RL, planning, and control stacks. This role blends physics intuition, ML modeling, and hands-on experimentation to ensure digital twins remain stable, accurate, and continuously updated as real systems evolve. Responsibilities:Build model-identification and parameter-estimation pipelines with adaptive calibration.Develop ML-based dynamic models, multi-scale physics approximators, and hybrid simulation frameworks.Maintain twin fidelity, stability, and version consistency as data and hardware change.Work closely with simulation, RL, controls, and agent teams to integrate twins into decision-making and learning workflows. Qualifications:Strong experience creating or calibrating digital twins or dynamic, data-driven physics models.Knowledge of system identification, time-series modeling, and physical parameter estimation.Ability to combine physics, ML, and experimental data into robust predictive models.Comfort operating across ML, simulation tooling, and physical hardware interfaces in a fast-paced environment.

Our client is building advanced AI systems with real physical capability. Their work spans experimentation, engineering and automated manufacturing, and they have already delivered large scale projects in the public and private sector. This is a team that invents from first principles and builds end to end systems that push the frontier of physical AI.They are now searching for a Senior ML Infrastructure / MLOps Engineer to design, operate and scale the backbone that powers large model development. Your work will shape the training, fine tuning and deployment infrastructure across LLMs, RL agents and physics-driven surrogate models.The roleYou will own the systems that enable large scale training, RLHF and DPO workflows, dataset governance, experimentation, reproducibility and model deployment. This includes distributed training design, containerized model runners, data and versioning pipelines, and evaluation automation that keeps model development reliable and fast.ResponsibilitiesBuild and maintain scalable infrastructure for training, fine tuning and distributed ML workflows.Develop dataset pipelines, versioning systems, experiment tracking and reproducibility frameworks.Operate containerized training and inference environments, including CI/CD for models and evaluation tooling.Partner closely with researchers, RL teams, data engineering and systems engineers to support rapid iteration and robust deployment.What they’re looking forStrong experience in ML infrastructure, distributed training, experiment management or production ML systems.Comfort with containerization, orchestration, dataset governance and model evaluation pipelines.Ability to design reliable, high throughput training and deployment workflows.Someone who enjoys working across ML, infra and data systems in a fast moving research environment.

Senior RL Research Scientist / Reinforcement Learning ScientistJoin a frontier AI team building systems that can act in the physical world, experimenting, optimizing, and controlling real processes through advanced ML, simulation, and automation. This group is pushing the boundaries of physical intelligence, backed by significant long-term funding and a mandate to invent from first principles. If you want to:Work on problems few teams in the world can touchBuild RL systems that power real tools, workflows, and scientific processesOperate in a fast, high-ownership, deeply technical culture…this is the kind of role that defines a career. The Role You’ll design and deploy reinforcement learning systems that control complex tools, optimize multi-step processes, and operate across high-fidelity simulations and digital twins. Expect hands-on research, real-world experimentation, and tight collaboration with teams across ML, simulation, and systems engineering. What You’ll DoBuild RL environments for tool control, workflow optimization, and long-horizon decision-makingDevelop safe and constrained RL methods, verifier-driven rewards, and offline to online training pipelinesCreate state/action representations and evaluation frameworks for reliable policy behaviorWork with cross-functional researchers and engineers to deploy RL agents into real workflowsWhat You BringStrong background in RL, optimal control, or sequential decision-makingExperience applying RL to complex simulated or physical systemsFamiliarity with safe/constrained RL, verifiers, or advanced evaluation pipelinesAbility to design environments, rewards, and diagnostics at scaleComfort working across ML, simulation, and systems interfaces

Senior LLM Research ScientistA frontier-stage research group is building a new class of AI systems designed to reason, plan, and act across the physical world. Their mission is to create intelligent agents capable of experimenting, engineering, and constructing in ways that dramatically accelerate scientific and industrial progress. This team combines deep technical pedigree with real-world wins at scale, including major government-funded initiatives. They operate where advanced model research meets robotics, simulation, and automated engineering systems, offering the kind of impact only possible when first-principles science meets ambitious execution. Joining means stepping into a high-ownership environment where you shape core capabilities end-to-end, influence the direction of physical-world intelligence, and help build technology the world has never seen before. Why This Role Is CompellingWork on cutting-edge reasoning, planning, and tool-use models that directly control autonomous engineering systems.Push the limits of SFT, RLHF, DPO, verifier-guided RL, and long-horizon planning in a setting where your research immediately translates into real-world capability.Operate in a high-velocity research culture with exceptional peers across agent systems, simulation, data, and complex toolchains.Have outsized ownership in a small team tackling one of the most ambitious technical problems of this decade.Role Overview The team is looking for an LLM Research Scientist to pioneer next-generation reasoning and agent architectures. Your work will span model design, alignment strategies, structured tool orchestration, and experimentation with agents interacting across real engineering workflows. This position blends deep research with hands-on systems integration, offering both autonomy and scope to lead foundational progress. Key ResponsibilitiesDevelop advanced models and prompting systems for planning, multi-step reasoning, and structured tool use.Lead training initiatives across SFT, RLHF/DPO, verifier-guided RL, and modular expert architectures to strengthen robustness and controllability.Define schemas, tool-calling strategies, policy constraints, safety mechanisms, and recovery pathways for agent behavior.Partner closely with engineering, simulation, and data teams to test, train, and evaluate models embedded in real production-like toolchains.QualificationsSignificant experience in LLM research, agent reasoning models, or structured tool-use frameworks.Strong background working with SFT, RLHF, DPO, or reinforcement-learning-from-verification methods.Demonstrated ability to design, analyze, and improve long-horizon behaviors and decomposition strategies.Comfortable working across ML research, systems engineering, and real-world experimentation in a fast-moving environment.A track record of excellence and ownership in technically demanding domains.