Applied AI Engineer  I am working with a fast growing AI company building an enterprise grade AI workspace used by major financial institutions to produce and validate client ready work. The platform replaces complex manual workflows with automated AI systems that scale across global teams and has grown rapidly with backing from top tier investors. This role is for engineers who want to build and ship production systems. You will own core parts of the AI agent infrastructure, including multi agent systems, RAG pipelines, and evaluation frameworks. The work is hands on and production focused, covering backend services, AI infrastructure, and delivery at scale. What you will doBuild and deploy backend services and APIs, Python preferred using Django or FastAPIProductionise AI features including RAG, agent orchestration, and evalsCreate data pipelines for training, evaluation, and continuous improvementEnsure performance, reliability, and security across the stackWork closely with founders, engineers, and product teamsWhat we are looking forFive plus years of software engineering experienceProven experience deploying AI applications into productionStrong backend engineering skills and database fundamentalsExperience with cloud infrastructure, Docker, Kubernetes, and CI CDBackground workers, task queues, and Redis experienceFamiliarity with LLM evaluation, monitoring, and safetyDegree from a Russell Group university or equivalent top tier academic background, or alternatively extensive engineering expertise with clear, relevant production experienceThis is a demanding, in office environment with high ownership, shifting priorities, and strong technical standards. You will work directly with founders who have built and exited venture backed companies. If you are an Applied or Agentic AI Engineer looking for real ownership and the chance to build core systems from the ground up, this is worth a conversation.

We are looking for a GenAI Engineer to join a growing Consulting organisation focused on AI solutions for the varied industries. You will play a key role in developing and integrating enterprise-level AI systems, contributing to the next generation of intelligent tools used by their clients. What You’ll DoDesign, build, and deploy GenAI applications using OpenAI APIs and LLM frameworksDevelop and optimise RAG pipelines for production useCollaborate with cross-functional teams to integrate AI into existing SaaS productsWrite clean, efficient, and scalable code, primarily in PythonContribute to architecture and design discussions around AI deployment and automationEngage with clients and internal teams to ensure alignment on project goalsWhat We’re Looking ForProven background in software development within SaaS or enterprise environmentsStrong practical experience using OpenAI APIs in commercial or large-scale settingsSolid understanding of LLMs, prompt engineering, and model deploymentHands-on experience with RAG pipelines and data retrieval optimisationExcellent communication and stakeholder management skillsAble to work independently and within collaborative teamsNice to HaveFrench for collaboration with teams in LausanneGerman for client interactions in ZurichWhy JoinFully remote flexibility with the option to work near Zurich or LausanneStable, long-term AI projects within the financial sectorClear growth trajectory with opportunities to contribute to upcoming initiativesSupportive, collaborative environment with positive team sentiment

Senior / Principal Research Scientist – Core AI Algorithms (Autonomous Systems)Location: Germany (Remote-first within Germany, on-site in Frankfurt every 2–4 weeks)About the RoleWe are partnering with a global automotive OEM building a core AI research and algorithm team responsible for the foundational intelligence behind next-generation automated driving systems.This role is research-driven and sits upstream of product teams. The focus is on inventing, validating, and transitioning new perception and world-modeling algorithms from research into production-ready systems. The team operates similarly to a big-tech research lab, but with a clear path to real-world deployment.Research Focus AreasDepending on background and interest, you may work on topics such as:3D scene understanding and world modelingOccupancy, motion forecasting, and dynamic scene reconstructionMulti-sensor perception (camera, LiDAR, radar)Representation learning for autonomous systems (BEV, implicit / generative 3D, Gaussian models, foundation models)Robustness, generalization, and long-tail perceptionLearning under weak, sparse, or noisy supervisionBridging offline training with real-world deployment constraintsKey ResponsibilitiesConduct original research in perception and autonomous systems with clear technical ownershipDesign and prototype novel algorithms and learning frameworksPublish at or contribute toward top-tier conferences and journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICRA, IROS)Translate research ideas into scalable, production-oriented implementationsCollaborate with applied ML, systems, and hardware teams to ensure feasibilityShape the long-term technical roadmap of the core AI organizationMentor junior researchers and engineers where appropriateRequired BackgroundPhD (or equivalent research experience) in Computer Vision, Machine Learning, Robotics, or a related fieldStrong publication record at top-tier conferences or journalsExperience conducting research within an industrial or applied settingExcellent understanding of modern deep learning methods and 3D perceptionStrong programming skills in Python and/or C Ability to work across the full spectrum from theory to implementationStrongly PreferredResearch experience in autonomous driving, robotics, or embodied AIWork on 3D perception, tracking, SLAM, or world modelsExperience at big-tech research labs, industrial AI labs, or advanced OEM R&DFamiliarity with real-world constraints such as runtime, memory, and system integrationPrior collaboration with product or engineering teamsWhat’s on OfferA research-first role with real influence on production systemsThe opportunity to define core algorithms, not just incremental improvementsA team culture that values publications, patents, and long-term thinkingRemote-first working model within Germany, with regular in-person collaboration in FrankfurtCompetitive compensation aligned with senior / principal research profilesWho This Role Is ForResearchers who want their work to ship into real vehiclesIndustry researchers seeking greater technical ownershipPhD-level candidates who enjoy both publishing and buildingProfiles combining academic depth with practical engineering maturityLooking forward to seeing your profile!

I am working on a Lead AI Developer role for a UK based team delivering AI solutions into complex, non technical environments. This is a hands on role for someone who codes daily but also leads from the front. You would sit between senior stakeholders and delivery teams, shaping requirements, explaining trade offs, and guiding technical direction without relying on formal authority. What the role actually needs. You are still a builder. Strong in C#, .NET, and Python, comfortable shipping production systems, deploying to cloud, and working with modern AI patterns like LLMs, RAG, and agent based workflows. At the same time, you are confident in front of clients. You can run a requirements session, challenge vague asks, surface constraints early, and translate technical decisions into language that non engineers trust. You have led delivery through influence. Mentoring developers, setting standards, steering architecture discussions, and handling competing priorities when stakeholders want different outcomes. What you would be doingWorking directly with clients to turn real world problems into clear technical designs and delivery plansLeading backlog refinement, sprint planning, and technical prioritisationBuilding and deploying AI enabled features across a Microsoft and Azure focused stackExplaining feasibility, risk, and trade offs in a way that helps stakeholders make decisionsRaising the bar for engineering quality through reviews, coaching, and exampleWhat tends to work well herePeople who have been the technical lead in client facing environmentsDevelopers who enjoy ambiguity and creating clarity rather than waiting for perfect specsEngineers who can say no when needed, and explain why in a constructive wayIf you are interested in this position, feel free to send your updated CV and we'll be in touch if this is a match.

AI Evaluation Engineer$180,000 Remote (US-based)Are you passionate about shaping how AI is deployed safely, reliably, and at scale? This is a rare opportunity to join a mission-driven tech company as their first AI Evaluation Engineer, a foundational role where you’ll design, build, and own the evaluation systems that safeguard every AI-powered feature before it reaches the real world.This organization builds AI-enabled products that directly helps governments, nonprofits, and agencies deliver financial support to people who need it most. As AI capabilities race forward, ensuring these systems are safe, accurate, and resilient is critical. That’s where you come in.You won’t just be testing models, you’ll be creating the frameworks, pipelines, and guardrails that make advanced LLM features safe to ship. You’ll collaborate with engineers, PMs, and AI safety experts to stress test boundaries, uncover weaknesses, and design scalable evaluation systems that protect end users while enabling rapid innovation. What You’ll DoOwn the evaluation stack – design frameworks that define “good,” “risky,” and “catastrophic” outputs.Automate at scale – build data pipelines, LLM judges, and integrate with CI to block unsafe releases.Stress testing – red team AI systems with challenge prompts to expose brittleness, bias, or jailbreaks.Track and monitor – establish model/prompt versioning, build observability, and create incident response playbooks.Empower others – deliver tooling, APIs, and dashboards that put eval into every engineer’s workflow. Requirements:Strong software engineering background (TypeScript a plus)Deep experience with OpenAI API or similar LLM ecosystemsPractical knowledge of prompting, function calling, and eval techniques (e.g. LLM grading, moderation APIs)Familiarity with statistical analysis and validating data quality/performanceBonus: experience with observability, monitoring, or data science tooling

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.

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

LLM Evaluations Engineering LeadSF Bay Area (Onsite) Full-time / Permanent We’re partnering with a deep-tech AI company building autonomous, agentic systems for complex physical and real-world environments. The team operates at the edge of what’s possible today, designing AI systems that plan, act, recover, and improve over long horizons in high-stakes settings. They’re hiring an LLM Evaluations Engineering Lead to own the evaluation, verification, and regression layer for agentic LLM systems running end-to-end workflows. This is not a metrics-only role. You’ll be building the guardrails that determine whether the system is actually getting better.Why this role mattersAs agentic LLM systems move into long-horizon planning and execution, evals become the bottleneck. This role defines whether:Agents are actually improvingChanges introduce silent regressionsUncertainty is shrinking or compounding“success” reflects real-world outcomes, not proxy metricsIf evals are wrong, everything downstream is wrong. This role sits directly on that fault line.What you’ll doBuild eval harnesses for agentic LLM systems (offline in-workflow)Design evals for planning, execution, recovery, and safetyImplement verifier-driven scoring and regression gatesTurn eval failures into training signals (SFT / DPO / RL)What they’re looking forStrong experience building evaluation systems for ML models (LLMs strongly preferred)Excellent software engineering fundamentals:PythonData pipelinesTest harnessesDistributed executionReproducibilityDeep understanding of agentic failure modes, including:Tool misuseHallucinated evidenceReward hackingBrittle formatting and schema driftAbility to reason about what to measure, not just how to measure itComfortable operating between research experimentation and production systemsWhy joinWork on frontier agentic AI systems with real-world consequencesOwn a foundational layer that determines system reliability and progressHigh autonomy, strong technical peers, and meaningful equityBuild evals that actually matter, not academic benchmarks