OpportunityWe’re partnering with an ambitious, investor-backed health technology scale-up that is building a next-generation preventive healthcare platform powered by machine learning, multimodal data, and conversational AI.This is a rare chance to join as one of the earliest technical hires and help shape both the product and the company. You’ll work directly with founders and senior technical leaders, taking ownership of problems that sit at the intersection of AI, healthcare, and product innovation.If you enjoy building from first principles, shipping quickly, and seeing your work reach real users, this role offers an exceptional level of autonomy and influence.About the OrganisationThe business is developing technology that uses everyday consumer devices to make health screening and early risk detection dramatically more accessible. Their long-term vision is to create a trusted, intelligent health platform that combines digital signals, clinical data, and AI-driven decision support to help people identify potential issues earlier and engage with appropriate care pathways.Backed by experienced investors and led by a team with deep expertise in machine learning, biostatistics, and healthcare technology, the company is now entering an exciting phase of product expansion and AI development.The Impact You'll HaveAs a Founding AI Engineer / Data Scientist, you won’t be maintaining existing models—you’ll be helping define what the AI stack becomes.You will contribute to customer-facing capabilities such as:Personalised recommendation systemsMultimodal biomarker estimation using video, audio, text, and behavioural signalsPredictive health modellingAI-powered clinical reasoning and protocol generationFoundational components for future conversational medical agentsYou’ll also help establish the company’s ML infrastructure, deployment practices, and data foundations as the platform scales.Key ResponsibilitiesDesign, train, evaluate, and deploy machine learning models across multimodal datasets.Build production-grade ML systems using Python and PyTorch.Develop recommendation and decision-support capabilities that deliver measurable user value.Work with large, heterogeneous datasets including audio, video, imaging, sensor, and clinical data.Create robust data pipelines and contribute to the overall ML platform architecture.Prototype and integrate modern AI techniques, including agentic and reasoning-based systems.Collaborate closely with product, engineering, and domain experts to translate research into usable features.Help define engineering standards, experimentation practices, and model monitoring processes.Stay current with advances in applied AI, healthcare ML, and production ML tooling.What We're Looking ForMSc or PhD in Machine Learning, Statistics, Computer Science, or a related quantitative discipline.Strong industry experience in applied ML or data science (typically 4+ years, or 1+ year post-PhD).Excellent Python development skills and strong software engineering fundamentals.Hands-on experience with PyTorch or comparable deep learning frameworks.Solid understanding of statistical modelling, machine learning, and experimental design.Experience building or deploying ML systems in production.Familiarity with recommendation systems, signal processing, or multimodal modelling.Comfort working in a fast-moving, high-ownership environment.Strong written and spoken English.DesirableExperience in healthcare, life sciences, or another regulated industry.Published research or significant applied research experience.Experience with LLM orchestration, AI agents, or reasoning architectures.Knowledge of MLOps, model monitoring, and scalable deployment practices.Exposure to clinical or biomedical datasets.Why Join?This is the kind of role where your decisions will matter immediately.You’ll be joining a small, highly capable team that values initiative, curiosity, and practical problem-solving. The environment is designed for people who want to build rather than wait for permission.In return, you’ll get:Genuine ownership of critical AI and data initiatives.Direct access to founders and strategic decision-making.The opportunity to help shape an AI-first healthcare product from an early stage.A mission with clear real-world impact.Exposure to cutting-edge work across multimodal AI, digital health, and conversational systems.A collaborative, low-ego culture focused on learning and execution.

An AI company delivering end-to-end AI solutions—spanning foundation models, developer tooling, and enterprise-grade applications—is hiring a Technical Lead for its Applied AI function. This person will oversee delivery of advanced AI systems for large customers operating in regulated or high-complexity sectors such as banking, healthcare, and industrial production. What This Role Involves This is a player-coach position: you'll manage a small team of AI engineers while staying directly involved in building and reviewing code for the most technically demanding pieces of each engagement. You'll be the go-to technical contact for key accounts, spanning early scoping conversations through to rollout and support, and you'll coordinate closely with teams in research, product, and engineering. Core ResponsibilitiesContribute directly as an engineer on high-stakes workstreams (model fine-tuning, retrieval-augmented generation, multi-step agent systems, bespoke LLM builds)Guide and coach a group of applied engineers, establishing technical direction and quality barsSupport pre-sales by turning client requirements into workable technical proposalsTrack new developments in the field and bring promising approaches into the team's toolkitLoop customer learnings back to product and engineering to shape what gets built nextCandidate ProfileStrong written and spoken EnglishMaster's or doctoral degree in machine learning, computer science, or an adjacent discipline7+ years working in AI/ML, with 2+ years spent in a lead capacity (e.g., Engineering Manager, Tech Lead, Solutions Architect)History of shipping AI systems end-to-end, from early prototype to live production useSolid grounding in fine-tuning approaches, sophisticated RAG pipelines, and agent-based architectures at scaleHands-on coding ability in Python and PyTorch, plus familiarity with common ML toolingBackground in backend or full-stack engineering, including API and systems designComfortable presenting technical material to both engineers and non-technical leadershipAlso ValuableInvolvement in open-source AI/ML projectsPrevious work in a client-facing capacity (e.g., Solutions Engineer, Technical Account Manager)Experience shaping a product roadmap using direct customer input

Senior Edge Systems Engineer Location – Paris, France. Onsite. We're working with an ambitious technology company that's applying AI to solve complex challenges in healthcare. They're looking for a Senior Edge Systems Engineer to help deliver reliable, high-performance systems that operate across multiple customer sites. The Role You'll take ownership of the software and infrastructure that supports edge devices in the field, ensuring they remain connected, secure, and easy to manage. From diagnosing connectivity issues to improving system performance and scalability, you'll play a key role in keeping critical technology running smoothly outside of the traditional cloud environment. Working alongside engineering and infrastructure teams, you'll help build solutions that support large-scale video and data processing while improving monitoring, automation, and remote support capabilities. What You'll BringCommercial experience with Linux-based systems, edge computing, embedded platforms, or infrastructure engineering.Strong understanding of networking, system diagnostics, and troubleshooting distributed environments.Experience working with video processing, streaming technologies, or high-volume data transfer is highly desirable.Ability to automate workflows using Python, Bash, C++, or similar languages.Familiarity with containerisation, remote device administration, or cloud-connected hardware.A proactive, problem-solving approach and the confidence to work in a fast-moving environment.Bonus ExperienceNVIDIA Jetson or similar edge hardware.Docker, IoT, embedded Linux, or fleet/device management.Exposure to healthcare, medical technology, or other highly regulated industries.If you're excited by the challenge of building dependable edge systems that support innovative AI solutions in real-world environments, we'd love to hear from you.

Machine Learning Researcher, Audio $200,000 – 250,000+, Equity + Bonus Remote (US & Europe) / San Francisco, CA (Hybrid preferred) Full-time / PermanentDeepRec has partnered with a fast-growing, revenue-generating voice AI company empowering enterprises to build AI phone agents at scale. Recent Series C funding with backing from leading Silicon Valley investors, they are building the models and infrastructure that make voice the primary interface between businesses and their customers.This company has built all of their current models completely in-house, and every model ships to real, paying customers almost immediately. No speculative research track here. If you want your work to hit production within weeks, not years, this is that role. The OpportunityThe research team are working toward a single, ambitious goal: a fully speech-to-speech conversational AI model that understands and responds like a human, in real time. You'll work across the core building blocks of that roadmap, such as: speech-to-text, text-to-speech, neural audio codecs, and getting LLMs to understand and reason over audio directly.You'll take ideas from theory through large-scale training to production inference serving millions of calls a day, working closely with engineering and product teams to get your research into real customer environments fast. What You'll DoBuild TTS models that sound natural, expressive, and humanBuild STT systems that stay accurate with accents, background noise, and messy phone linesWork on neural audio codecs, compressing audio efficiently without losing qualityExplore LLM-Audio UnderstandingPrepare and manage large audio datasets, and design how models are trained on themRun training across many GPUs at once, keeping an eye on cost and speedRun fast, well-designed experiments to test what actually worksMake sure models run fast and reliably in production, not just in benchmarksWhat You'll Bring EssentialA genuine, self-driven interest in this area of research, shown through your own projects or papers, not just what a past employer asked you to doHands-on experience building or improving TTS, ASR, speech-to-speech, or neural audio codec systemsA track record of original, hands-on technical work, rather than off-the-shelf tools or common tutorial-style projectsStrong Python skills and experience training or running large modelsComfortable working autonomously DesirableExperience getting LLMs to understand or reason about audio - the team's top priority right nowExperience with model distillationExperience fine-tuning or training language or speech models with reinforcement learningPublished research or open-source work in speech or language AIBackground working with real-time speech systems or phone-based systemsA PhD is welcome but not required. Strong, independent work matters more than qualificationsWe encourage you to apply even if you don't meet every requirement. The right mindset and genuine curiosity matter as much as the resume. What's In It For YouGround-floor seat in a lean and growing research team with real scope to shape it as it scalesEvery research output ships to real customers, no long speculative research projectsHigh autonomy to shape your own research direction, tooling, and (for senior hires) the team itselfWork across ASR, TTS, neural codecs, and the frontier of LLM-audio understanding & speech-to-speech modellingWell-capitalised, fast-moving environment without big-lab bureaucracyHealthcare, dental, vision, meaningful equity, and every tool you need to succeedRemote-friendly across the US

About the roleWe are supporting a Munich-based deep-tech company building low-power distributed sensing devices.Their devices need to communicate over long distances while remaining power-efficient and robust in challenging environments.They are looking for an RF / Communications Engineer to work alongside their current communications engineer and help take ownership of the wireless networking side of the platform. This role sits between RF, embedded systems and IoT networking.What you’ll doWork on low-power wireless communication for distributed sensing devices.Develop and improve LPWAN / long-range communication systems.Work with sub-GHz communication technologies.Support routing, networking and communication protocols across distributed nodes.Help improve resilience against interference, jamming and spoofing.Work closely with embedded systems and hardware teams.Support system-level testing and debugging of communication performance.Help scale the communications architecture as the device network grows.What they’re looking forExperience with RF communications or wireless embedded systems.Understanding of LPWAN, sub-GHz communication or long-range IoT networks.Knowledge of routing, networking or distributed communication systems.Comfortable working with low-power devices.Ability to work close to hardware and embedded engineering teams.Practical engineering mindset with strong debugging ability.Nice to haveExperience with jamming/spoofing resilience.Experience in defence, aerospace, drones, robotics or industrial IoT.Experience with LoRa, LoRaWAN, mesh networking or proprietary sub-GHz protocols.Experience with field testing wireless communication systems.

About the roleWe are supporting a Munich-based deep-tech company building distributed acoustic sensing systems. Their technology needs to be tested in real-world environments, including field trials, drone tests and controlled chamber testing.They are looking for a Flight Test / Data Engineer who can take ownership of the data side of these tests. This person does not need to come from a flight test background; the most important skill is being able to work with real-world sensor data, process it properly, structure it, score it and identify errors in the data pipeline.What you’ll doSupport acoustic data collection from field tests, drone tests and chamber testing.Work with drone operators and interns to conduct structured test campaigns.Record, process and organise test data.Perform post-processing and quality checks on acoustic sensor data.Score and structure results in databases.Identify, debug and document errors in the data collection and processing flow.Help improve how test data is captured, labelled, stored and used by engineering teams.Work closely with ML, embedded and hardware engineers to make test data usable.What they’re looking forStrong data engineering or data processing experience.Comfortable working with messy, real-world sensor data.Experience with databases and structured data workflows.Strong Python or similar scripting skills.Ability to debug errors across data collection, storage and processing.Practical mindset; happy to be close to real-world testing rather than only working behind a desk.Interest in learning flight testing and drone-based data collection on the job.Nice to haveExperience with acoustic data or signal processing.Experience with drone testing, robotics testing or field testing.Experience in defence, aerospace, robotics, autonomous systems or industrial sensing.Experience supporting ML teams with high-quality labelled/structured data.

About the roleWe are supporting a Munich-based deep-tech company building a distributed sensing and AI platform. Their hardware collects real-world sensor data, which needs to be stored, processed, managed and connected through a robust cloud infrastructure.They are now looking for an internal Cloud Infrastructure Engineer to own their AWS cloud setup from first principles. External partners have helped define proposals and support DevOps execution, but the company now needs someone internally who can own the architecture, strategy and long-term knowledge base.What you’ll doOwn the AWS cloud infrastructure strategy and architecture.Build cloud infrastructure from the ground up.Define scalable, secure and maintainable cloud systems.Work with Terraform or similar infrastructure-as-code tooling.Coordinate external cloud/devops partners and review their work.Take ownership of cloud decisions around EU data sovereignty and infrastructure design.Support ML/data infrastructure for sensor data and AI workflows.Build internal knowledge so cloud ownership does not sit only with external contractors.Decide what should be built internally versus delegated externally.What they’re looking forStrong AWS infrastructure experience.Experience building cloud infrastructure from scratch, not just maintaining a small part of an existing system.Strong Terraform / IaC experience.Good DevOps fundamentals.Comfortable owning architecture, strategy and hands-on implementation.Experience working with external engineering partners or contractors.Ability to think like a builder: practical, hands-on, and comfortable creating structure where little exists yet.Nice to haveML infrastructure or MLOps experience.Experience with sensor data, IoT data or edge-connected devices.Experience with EU sovereignty, security or regulated data environments.Startup or early-stage company experience.

About the roleWe are supporting a Munich-based deep-tech company building distributed sensing systems that combine low-power embedded hardware, acoustic data acquisition, wireless communication and AI at the edge.They are looking for a full-time Electronics Engineer to take ownership of the electronic design work behind their main sensing node. Until now, parts of the electronics work have been handled by a mix of internal engineers, external design partners and contractors. The goal now is to bring this knowledge in-house and have one person own the design, development and ongoing improvement of the hardware platform.What you’ll doOwn electronic design for low-power embedded sensing systems.Create and maintain schematics for new PCB versions.Work on low-noise analogue data acquisition systems.Support PCB design, routing decisions and design reviews.Maintain the internal electronics knowledge base.Work closely with embedded systems, RF/comms and ML teams.Help improve reliability, power consumption and manufacturability of the sensing node.Interface with external electronics partners where required, while gradually bringing knowledge in-house.What they’re looking forExperience designing electronics for embedded, IoT or low-power connected devices.Strong schematic design experience.Good understanding of analogue systems and low-noise data acquisition.Experience with PCB development and iteration cycles.Comfortable working across sensors, batteries, networking and edge hardware.Able to take ownership rather than only contribute to small isolated design tasks.Practical mindset; someone who enjoys building, testing and improving physical products.Nice to haveExperience with acoustic sensing hardware.IPC Class 3 exposure.Experience with battery-powered devices.Experience in defence, robotics, industrial IoT, wearables or connected sensor systems.