Field Enablement Emea Manager Jobs

Lead AI Engineer (Senior/Principal Consultant) Location: London (Hybrid) | Practice Area : Technology & Engineering | Type: Permanent Empower the next frontier of AI with Capco. Shape, build, and deliver future-ready generative systems. The Role We’re looking for a Lead AI Engineer (Senior/Principal Consultant) to join our Technology Delivery Team. You’ll combine your expertise in AI/ML engineering and software development to build and deploy cutting-edge generative AI and agentic systems across the financial services sector. As a hands-on leader, you’ll architect AI solutions, embed them within enterprise environments, and drive impactful outcomes through advanced engineering, scalable pipelines, and deep cross-functional collaboration. What You’ll Do Architect and develop autonomous AI systems integrating multi-modal LLMs (text, image, audio, video) Design agentic workflows enabling AI agents to interact with data sources and APIs using prompt engineering and RAG Fine-tune, optimize, and deploy large language and multi-modal models with a focus on performance and latency Build scalable MLOps pipelines and full-stack applications to support robust, production-grade AI deployments Guide multidisciplinary teams and clients through strategic decisions in AI engineering and GenAI adoption What We’re Looking For Bachelor’s degree or higher in Computer Science, AI, or related STEM fields Hands-on experience deploying LLMs and multi-modal models at scale Strong engineering background in Python with proven backend and API development skills Solid understanding of scalable MLOps, observability, and cloud-native AI deployment Excellent communication, problem-solving, and project management skills in agile environments Bonus Points For Experience with agentic frameworks (e.g., LangChain, LlamaIndex) Experience in deep learning frameworks and front-end development Familiarity with Langfuse, Langsmith, or other LLM observability tools Understanding of Model Context Protocol

The Behavior Capabilities opportunity falls within the Fail Operational Planning team at Zoox. This team works to ensure that the robot continues to operate effectively, even in the presence of minor compartmentalized failures or degraded capabilities. The focus of the team includes developing behavioral and motion planning algorithms to improve trip progress, vehicle uptime metrics, and correspondingly a better customer experience. This is a cutting-edge problem which requires teamwork and collaboration across a diverse group of experts including Planner, Perception, Prediction, Hardware, and more. On this team, you will contribute to the next generation of algorithmic advancements to expand our robot’s ability to drive in some of the most complex scenarios. We are looking for an engineer who is excited to understand the interdependencies within the driving stack and takes ownership of their projects and has a holistic product focused approach towards development. Responsibilities Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base. Collaborate cross-functionally to develop strategies and intelligent behaviors to mitigate the performance degradation under various failure modes of the system. Architect and implement scalable solutions ensuring seamless integration with the rest of the driving stack. Partner with validation teams to define and execute tests for developed algorithms. Qualifications Master's degree in Computer Science or related field Fluency in C++ Extensive experience with programming and algorithm design Understanding of vehicle kinematics 4+ years of experience in a related field Bonus Qualifications PhD in Computer Science, Electrical Engineering, Mechanical Engineering, or related field Experience shipping products to market (commercial robotics, drones, consumer devices etc.

Our Perception team is responsible for using our sensor data to understand the complex and dynamic environments where we drive. In this role, you will have access to the best sensor data in the world and an incredible infrastructure for testing and validating your algorithms. We are creating new algorithms for segmentation, tracking, classification, and high-level scene understanding, and you could work on any (or all!) of these components. As a Senior Staff ML Engineer, you will lead the development of machine learning algorithms that can range in influence from onboard autonomy to offboard autonomy and validation. You will collaborate closely with other teams specializing in Prediction, Planning, Simulation, and Safety Validation, influencing our overall technical stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company. The impact of the role can be in the form of impacting immediate company milestones to leading forward-looking exploratory projects. Responsibilities Develop new algorithms to understand the scene around the robot, and how that scene would evolve through time Build multi-modal foundation models for on-vehicle and offline applications Develop new algorithms to apply generative AI to simulation to improve the realism of our offline validation systems Leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field Provide technical mentorship to the broader group of ML developers at Zoox Collaborate with engineers on Prediction, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Experience with training and deploying Deep Learning models on sensor data-Experience

The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance. As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior. In this role, you will: You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc. You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field You will develop metrics and tools to analyze errors and understand improvements of our systems You will collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Experience with training and deploying transformer-based model architectures and reinforcement learning Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines Fluency in C++

The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance. As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior. In this role, you will: You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc. You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field You will develop metrics and tools to analyze errors and understand improvements of our systems You will collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Experience with training and deploying transformer-based model architectures and reinforcement learning Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines Fluency in Pyt

Please send CVs to [email protected] with “202 7 QR Summer Internship Application” in the subject line. When your application is received, we will consider you for all similar positions at Cubist. About Our Firm: Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. About Our Team: KEPL is a fast-growing team at Cubist Systematic Strategies. We specialize in trading medium-frequency statistical arbitrage strategies with high Sharpe. The team is made up of people from top universities and top tier trading and tech firms. We have an open and collaborative culture, and we value rigorous research and innovative technologies. Role / Experience: We are looking for exceptional students to be our quantitative researcher interns for the summer of 2027 . An ideal candidate should have a strong passion and initiative to work in a start-up environment. He/she should have strong analytical skills and be able to solve hard problems rigorously. Our typical intern candidates come from quantitative PhD programs of top US universities. Our internship program offers unique KEPL experience. During the internship, our interns will receive rigorous and comprehensive trainings. They will develop strong research skills through working closely with our full-time researchers on brand new quant trading models with real-world impact. We will consider full-time offers for interns after the internship. Requirements: PhD candidate in math/physics/statistics/EE/CS, or other quantitative fields Strong knowledge of computational math, probability, and statistics Strong analytical skills, with attention to details Good communication skills Willing to work in a fast-paced start-u

About the Team: A well-established quantitative portfolio management team at Point72 is looking for an experienced quantitative professional in the intraday to mid frequency systematic macro space. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business. Role: Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets Work with price-volume and alternative data at intraday to multiday (up to 2-3 weeks) horizons in the mid-frequency space Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets Responsibilities: Develop systematic trading models across global futures (equity indices, commodities and fixed income) and/or FX markets Alpha idea generation, backtesting, and implementation Evaluate new datasets for alpha potential Contribute to and enhance portfolio optimization, allocation and risk management processes Help drive the growth of the investment process and research capabilities of the team Assist in building, maintenance, and continual improvement of production and trading environments Requirements: MS or PhD in physics, engineering, statistics, applied math, quantitative finance, or other quantitative fields with a strong foundation in statistics 4+ years of signal research or portfolio management experience in futures markets and/or FX as part of a successful proprietary trading team with a track record Prior professional experience with signal combination, portfolio optimization and risk management Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas Collaborative mindset with strong independent research abilities Commit

P-8 At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world’s best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Data Engineering is foundational and among the largest scale workloads on the Databrick Data Intelligence Platform. We are reinventing Data Engineering with Lakeflow - a unified product and experience for simple data ingestion, declarative data transformation, and real-time streaming. In this role, you will lead product management for a core Lakeflow product area. You will own and drive all aspects of product management including vision, strategy, roadmap, execution, and go-to-marketing. In addition, you will partner closely with various Databricks product teams to enable Data Engineering for the overall Databrick product portfolio including data science, data warehousing, business intelligence, and machine learning products. The impact you will have Lead product management for one of the fastest growing products and businesses at Databricks Make company wide impact by driving Data Engineering across the Databricks product portfolio Develop and deepen understanding of and expertise in Data Engineering, a foundational domain in the data and AI industry Define, shape, and drive the future of data processing, data applications, and data pipelines Own the full life cycle of product development from ideation to requirements, development, pricing, launch, and go-to-market. What We Look For 5+ years of product management and related experience with enterprise or SaaS products. Educational or professional background in computer science or related engineering fields. Ability to partner with senior technical leaders from Engineering, while going deep on technical concepts. Track record of delivering products

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Beyond the typical Scribe role, these important care team members serve as clinical documentation assistants to their paired provider. Internally, we call them CISs (Clinic Informatics Specialists) in recognition of their important role in supporting accurate, specific, and timely clinical documentation. In addition to observing and documenting all patient encounters in real time, our Scribes become experts in our value-based care model and the documentation and care of chronic conditions, including ICD-10 and CPT coding. Scribes use this expertise to help providers identify and help close care gaps. Scribes receive extensive on-the-job training in clinical workflows, value-based medicine, preventative care for chronic conditions, accurate and specific documentation, population health data streams, and team based care. Because our patients and providers rely on our Scribes, the ideal candidate should commit at least 1-2 years to this role. This is an excellent opportunity for pre-med track individuals looking to gain practical, paid experience in a clinical setting before applying to an MD/DO/PA/NP program, as well as those pursuing careers in Health Informatics, Public Health, Healthcare Administration, Medical Coding, and other related fields. Check out this <a href="https://drive.google.com/file/d/1xNEBBvLUXmNPLViIRhhqr2hGpjTZrvin/view?usp&#61;sharing" target="_blank"