Machine Learning

Data Scientist – 6 Month ContractLocation: Berlin, GermanyWorking pattern: Hybrid – 2 days per week in the officeStart date: November 2026Contract: 6 monthsRate: €80–€100 per hourInitial engagement: 4–5 months with potential to extendWe’re working with a growing European FinTech business looking for an experienced Data Scientist to join their team on a 6-month contract.This is a hands-on role focused on using data science, predictive modelling and simulation to solve complex commercial and financial problems.The RoleYou’ll be responsible for taking data-driven projects from initial analysis through to modelling, deployment and communicating the results to stakeholders.Key responsibilities include:Analyse structured and unstructured data to identify insights and opportunitiesTranslate business challenges into analytical hypotheses and data science solutionsDesign, train, deploy and monitor predictive modelsDevelop data-driven business cases and commercial modelsBuild and run simulation scenarios to assess revenue and business impactWork with stakeholders to understand business requirements and turn them into practical analytical solutionsCarry out ad-hoc analysis to answer complex business questionsCommunicate findings clearly to both technical and non-technical stakeholdersContribute to the end-to-end data science lifecycle, from data exploration through to deploymentWhat We're Looking ForStrong commercial Data Science experienceExcellent Python and SQL skillsStrong experience developing predictive modelsExperience working with both structured and unstructured dataStrong analytical and problem-solving abilityExperience with simulation, scenario modelling or revenue/pricing modelsAbility to translate business requirements into analytical solutionsStrong communication skills with the ability to explain complex concepts clearlyExperience working within financial services, FinTech, trading or financial marketsUnderstanding of financial products such as options, futures, equities or FX is highly beneficial

DeepRec.ai is representing a rapidly growing, venture-backed AI company that's building technology to help enterprises understand, improve, and automate complex business processes. Our client's team brings together experienced engineers, AI researchers, and operators with backgrounds building sophisticated AI systems at scale, and they place a premium on technical depth, intellectual curiosity, ownership, and a first-principles approach to hard problems. The Opportunity Our client is building a learning system that helps AI agents understand how enterprises actually operate. Their platform ingests information from sources like knowledge bases, conversations, support tickets, and system activity, then converts it into structured instructions that AI agents can execute — with built-in confidence and reliability mechanisms that determine when an agent should act autonomously versus loop in a human. We're looking to connect them with an AI Engineer who has shipped complex, production-grade LLM systems — whether that's scaling LLM workflows, building multi-agent systems, designing evaluation infrastructure, or developing AI products for demanding production environments. In this role, you'd spend most of your time advancing the company's core AI infrastructure and the systems powering intelligent agents across enterprise use cases, working across continuous learning, agentic workflows, human-in-the-loop feedback, evaluation, and orchestration. What You'd Be DoingBuilding and extending a core context-learning platform, turning real customer problems into reusable AI capabilitiesDesigning and implementing LLM-powered systems and agentic workflows from concept through productionBuilding autonomous agents for knowledge management — systems that can create, edit, update, and maintain large knowledge basesDeveloping reliability and confidence mechanisms, including evaluation frameworks and decision logic for automate-vs-escalate callsArchitecting asynchronous, scalable infrastructure to support complex AI orchestrationBuilding systems that learn and improve through human feedback, evaluation, and iterative optimizationContributing to the company's AI strategy, technical architecture, and product directionWhat Our Client Is Looking ForExperience building complex, production LLM-based systems, and the ability to speak to the engineering decisions and tradeoffs behind themA track record of shipping meaningful software or AI systems to production and iterating on them based on real-world usageExperience building agents, autonomous systems, or sophisticated LLM workflowsGenuine interest in systems that improve continuously through human feedback, evaluation, prompt optimization, and context engineeringComfort operating at the boundary between AI research and production engineeringStrong systems-design chops, particularly with asynchronous and distributed architecturesExcellent written and verbal communication — able to explain technical concepts to both technical and non-technical stakeholders3+ years of professional engineering experienceDon't meet every point on that list? Our client is open to exceptional engineers with unconventional combinations of skills and experience, so we'd still encourage you to apply. Compensation & BenefitsCompetitive base salary and meaningful equityComprehensive health, dental, and vision coverageFlexible PTOSupport for setting up a home workspaceOffice meals, snacks, and drinksAdditional location-appropriate benefitsWorking Environment This is a highly collaborative, fast-paced team with a strong emphasis on in-person collaboration.

Senior Reinforcement Learning EngineerZurich | Hybrid | Full-timeYou’ve already deployed reinforcement learning on real robots. Now you can apply that experience to autonomous excavators working across different machines, sites and soil conditions. You’ll join a Series A robotics company taking Physical AI into construction, with systems already deployed across multiple countries.You’ll build learning-based planning and control systems that work outside the simulator. That means improving simulation and sim-to-real transfer, designing data pipelines for real-world training, running experiments on physical machines and understanding why behaviour changes when conditions get messy. You’ll also integrate learned components into the wider autonomy stack and help shape how the system moves from prototype into a reliable product.This is a hands-on engineering role for someone with 2–5 years of industry experience in reinforcement learning for control or planning, who has actually deployed systems on physical robots. You’ll need strong Python and PyTorch skills, good C++, experience with GPU-accelerated simulation, and the ability to debug real-world robotic behaviour. Experience with hydraulic machinery, large-scale deployments, imitation learning or production rollout strategies would be useful.You’ll have genuine scope to influence the technical direction as the autonomy team builds a long-lived system designed to operate across the construction industry. If you want your RL work to move from simulation into machines doing real work, this is an opportunity to do exactly that.You’ll need:2–5 years’ industry RL experience in control or planningProven deployment on physical robotsStrong Python/PyTorch and good C++Experience with simulation and sim-to-realWillingness to travel when projects require itIf the challenge fits your background, let’s have a conversation about the role and the problems you’d be working on.