Research

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.

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.

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.

Foundation Model AI Architect (Molecular & Multimodal Systems)Our client is exploring a new generation of AI architectures grounded in principles from computational neuroscience, biological computation, and multimodal modelling. Their aim is to build large foundation models capable of reasoning over molecular, structural, and scientific datasets with explainability and precision.They are hiring a Foundation Model AI Architect to lead the design of advanced neural systems that combine transformer architectures, causal reasoning models, multimodal representations, and agentic behaviours. You will design models that integrate chemical data, molecular structures, spectroscopic signatures, and simulation derived information into unified AI systems for materials discovery.A key component of this role involves scaling models on high performance GPU clusters, optimising training and inference pipelines, and working with advanced frameworks such as JAX. You will also build automated labelling systems, behavioural encoding workflows, and interpretable ML pipelines that support transparency and scientific trustworthiness.This position suits someone who can translate ideas from systems neuroscience and complex biological modelling into practical, engineered AI architectures for real scientific problems.Ideal Profile:PhD in computational neuroscience or computational biology, deep expertise in neural architecture design, strong GPU/HPC programming skills, and experience developing large scale or multimodal foundation models.

Product Leader (Scientific Computing & AI Platform)Our client is building a sophisticated scientific platform that integrates automated quantum simulation, high throughput data workflows, and advanced machine learning for molecular and materials discovery. They are seeking a Product Leader to shape how these systems evolve and to guide the infrastructure that enables scientists and ML researchers to work seamlessly.You will define the long-term product vision, design roadmap milestones, and oversee how simulation tools, data pipelines, and AI models come together into a cohesive ecosystem. This includes responsibility for architecting automated DFT pipelines, real time inference systems, continuous integration frameworks, data streaming layers, and evaluation tooling for large scientific models.You will work across teams of computational chemists, simulation scientists, ML researchers, and software engineers to ensure the platform supports fast experimentation and high reliability. You will help shape neural architectures trained on molecular data, from GNNs to transformer-based models, and guide the integration of physics-based domain expertise into core AI workflows.This role requires strategic thinking, technical fluency, and comfort balancing scientific constraints with product execution. You will play a central role in defining the infrastructure that accelerates materials research across the organisation.Ideal Profile: PhD in a computational field, strong experience leading ML or scientific computing product systems, familiarity with automated quantum simulations, and deep understanding of large-scale AI and data tooling.

Cell Product Design EngineerOur client is developing high performance Li ion pouch cells for electric mobility, micromobility, and grid scale energy systems. As part of their continued expansion, they are looking for a Cell Product Design Engineer who can take full ownership of design, validation, and New Product Introduction (NPI) for next generation pouch cells.In this role you will lead the structural, electrochemical, and mechanical design of NCM based pouch cells. You will drive the entire lifecycle from concept design and form factor definition, through prototyping, validation, safety testing, and eventual mass production readiness. You will work across interfacial design, separator and electrolyte considerations, formation strategies, and internal architecture to meet performance, cost, and durability targets.You will collaborate closely with cross functional R&D teams, manufacturing engineers, test engineers, and program leads to ensure proper DFMEA, DVP&R, and quality processes are embedded throughout the design cycle. Your work will support products ranging from EV platforms to e bikes and stationary storage systems, requiring strong judgement, technical rigour, and the ability to manage complex tradeoffs.Ideal Profile: Degree in Materials Science or Chemical Engineering, hands on experience in pouch cell development and NPI, strong command of validation and quality methodologies, and the ability to bridge R&D and manufacturing environments.

Battery Product Development & Client Engagement LeaderOur client is a leader in advanced battery innovation, combining materials science, electrochemistry, and AI guided discovery to support global OEMs across mobility, energy storage, and electrification. As they scale international partnerships, they are seeking a technical leader to guide customer facing product development.This role blends technical ownership with client engagement. You will lead strategic programs with major OEM partners, define requirements for next generation battery technologies, and translate complex R&D findings into products that are manufacturable, scalable, and validated for demanding applications. You will guide the product roadmap, shape technical milestones, and coordinate tightly with R&D, materials engineering, testing, and manufacturing teams.Your work will span concept development, prototype definition, validation planning, DOE execution, safety analysis, failure mode assessment, and oversight of the lab to prototype to customer qualification pipeline. This is an opportunity to influence real world deployment of advanced battery systems and play a central role in aligning future technology with commercial needs.Ideal Profile:PhD in Chemical Engineering or Materials Science, strong background in electrochemistry and battery engineering, leadership experience with OEM programs, and expertise in process optimisation, failure analysis, and product scaling.