Machine Learning

Training Infrastructure Engineer Salary: €80,000 to €150,000 equity Location: Fully remote within Europe (CET ±2 hours) Stage: Recently funded Series A AI startup We are partnering with a fast-growing generative AI company building the next generation of creative tooling. Their platform generates hyper-realistic sound, speech, and music directly from video, effectively bringing silent content to life. The technology is already being used across gaming, video platforms, and creator ecosystems, with a clear ambition to become foundational infrastructure for audio-visual storytelling. Backed by top-tier venture capital and fresh Series A funding, the company is now scaling its core engineering group. This is a chance to join at a point where the technical challenges are deep, the scope is wide, and individual impact is unmistakable. The Role:As a Training Infrastructure Engineer, you will own and evolve the full model training stack. This is a hands-on, systems-level role focused on making large-scale training fast, reliable, and efficient. You will work close to the hardware and close to the models, shaping how cutting-edge generative systems are trained and iterated. What You Will Do:Design and evaluate optimal training strategies including parallelism approaches and precision trade-offs across different model sizes and workloadsProfile, debug, and optimise GPU workloads at single and multi-GPU level, using low-level tooling to understand real hardware behaviourImprove the entire training pipeline end to end, from data storage and loading through distributed training, checkpointing, and loggingBuild scalable systems for experiment tracking, model and data versioning, and training insightsDesign, deploy, and maintain large-scale training clusters orchestrated with SLURMWhat We Are Looking For:Proven experience optimising training and inference workloads through hands-on implementation, not just theoryDeep understanding of GPU memory hierarchy and compute constraints, including the gap between theoretical and practical performanceStrong intuition for memory-bound vs compute-bound workloads and how to optimise for eachExpertise in efficient attention mechanisms and how their performance characteristics change at scaleNice to Have:Experience writing custom GPU kernels and integrating them into PyTorchBackground working with diffusion or autoregressive modelsFamiliarity with high-performance storage systems such as VAST or large-scale object storageExperience managing SLURM clusters in production environmentsWhy This Role:Join at a pivotal growth stage with fresh funding and strong momentumGenuine ownership and autonomy from day one, with direct influence over technical directionCompetitive salary and equity so you share in the upside you help createWork on technology that is redefining how creators produce and experience contentIf you want to operate at the intersection of deep systems engineering and frontier generative AI, this is one of the strongest opportunities in the European market right now.

AI Engineer (Infrastructure) – London -Hybrid, £85k Stock Options Are you ready to help world-leading enterprises deploy cutting-edge AI at scale? We’re looking for an AI Infrastructure Engineer to join a pioneering AI company that’s transforming the way businesses operate. This isn’t just AI - it’s agentic AI that can drive decisions, optimize workflows, and unlock insights that were previously impossible. Trusted by Fortune 500 clients across finance, healthcare, and enterprise, this company has grown 500% year-on-year since emerging from stealth mode in 2021, backed by over $50M from top-tier investors and visionary angels. The Role You’ll be the bridge between enterprise infrastructure and AI innovation. Your focus will be on deploying advanced AI solutions into complex client environments, ensuring reliability, security, and scalability. Your expertise will determine how seamlessly it integrates into real-world systems. What You’ll DoCollaborate with top-tier enterprise clients to design and deploy bespoke AI workflows.Build robust architectures leveraging the latest AI technologies, including LLMs, RAGs, MCPs, and agentic workflows.Ensure enterprise-grade deployment across cloud and on-prem environments, with focus on availability, observability, and security.Translate complex business challenges into scalable, intelligent AI solutions.Serve as a forward-deployed engineer, working closely with both technical teams and business stakeholders.What You’ll BringProven experience deploying enterprise-grade AI or machine learning solutions in cloud and/or on-premise environments.Hands-on familiarity with at least one major cloud provider and strong DevOps skills.Client-facing experience with the ability to design, deliver, and support production-grade AI solutions.Strong solution architecture skills and a track record of driving measurable impact.Bonus points for personal AI projects or agentic AI experience.Why This Role Is ExcitingWork on high-visibility projects that truly change how enterprises operate.Join a company backed by world-class investors and trusted by global Fortune 500 clients.Be part of a team where 50% of members are PhDs, selected from over 50,000 applicants.Enjoy a collaborative, hybrid work environment (office Tues & Weds, optional additional days).BenefitsCompetitive salary (flexible for exceptional candidates)Share options and pension scheme25 days’ paid holiday plus bank holidays (carry over/sell up to 5 days)Work abroad days and flexible benefits, including Health/Dental InsuranceLearning & development budget, tech purchase support, office snacks, team events, referral bonusesInterview ProcessInitial screening callHiring manager/team interview (30-45 mins)Take-home challenge (3 hrs)In office interview - Speak with 3 members of the team (30 mins each), then a final chat with foundersThis is your chance to join a company that’s shaping the future of AI in enterprise. If you thrive in a high-impact environment where your work directly drives client success, this role is for you.

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: 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.

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.

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.

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.