What You’ll Do

We’re seeking a talented and experienced Senior Engineer for Network Observability to join our Network Observability team. In this role, you will be a key player in designing, developing, and maintaining the monitoring, telemetry, and observability systems that keep CoreWeave’s GPU cloud network operating reliably and at scale. You’ll focus on building solutions that provide real-time insights into network performance, ensuring that issues are detected proactively and resolved quickly.

Your mission? To empower CoreWeave’s network with advanced observability: robust metrics, powerful analytics, and automated alerting—so well-tuned that any anomalies become clear before they ever impact our customers.

• Develop, optimize, and maintain network observability platforms. Use your skills in Python and Golang to create and automate collectors, exporters, and dashboards that provide deep visibility into network health and performance.
• Collaborate with Network Engineering and Platform teams to ingest and unify logs, metrics, and events from a variety of platforms (Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, SR Linux, etc.) into a single observability pipeline.
• Design and implement scalable telemetry solutions using protocols like gNMI, SNMP, and streaming analytics. Ensure advanced alerting and anomaly detection with tools such as Prometheus, Grafana, and Alertmanager.
• Work closely with network developers, site reliability engineers, and security teams to integrate observability solutions across the broader infrastructure. Participate in design discussions, RFCs, and architectural decisions.
• Join a rotating on-call schedule to troubleshoot and resolve observability-related issues. Provide timely support to operations teams, quickly isolating and fixing problems when they arise.
• Guide junior team members, share best practices, and foster a culture of continuous learning and improvement within the observability domain.

Investing in our people is one of our top priorities, and we value candidates who can bring their diversified experiences to our teams. Here are some qualities we’ve found compatible with our team. We'd love to talk about whether this aligns with your experience and interests and what you’re excited to work on next.

Who You Are

Minimum Qualifications

• Deep familiarity with Prometheus, Grafana, Alertmanager, gNMI, and SNMP. Experience writing or extending custom metric collectors/exporters is a plus.
• Experience as a Network Engineer, SRE, Software Developer, or Systems Administrator in large-scale environments. A track record of building and operating robust telemetry and monitoring solutions is a plus.
• Passion for automating tasks and processes. You find satisfaction in creating workflows that handle repetitive tasks and reduce human error to near zero.
• Comfortable containerizing solutions in Kubernetes, designing, building, and deploying container-based workloads efficiently.
• Proficient with Python, Go, and Bash, plus familiarity with configuration management and templating tools (e.g., Ansible, Jinja2). .
• Strong knowledge of Linux systems and IP networking concepts, with hands-on experience in routing, switching, and network troubleshooting.
• Practical knowledge with a variety of platforms, including Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, and SR Linux.
• Collaborative, humble, and always ready to help others while staying open to learning from more senior colleagues.

Preferred Qualifications

• College Education: Bachelor’s degree in Computer Science or a related field.
• Machine Learning for Anomaly Detection: Hands-on experience applying ML techniques or tools (e.g., TensorFlow, scikit-learn) to proactively detect performance or security anomalies in network traffic.
• Network Certifications: Certifications like CCNA, CCNP, or similar.
• Advanced Metrics & Analytics: Hands-on experience with data pipelines, event correlation, or anomaly detection in large-scale environments.
• Distributed Tracing: Familiarity with OpenTelemetry, Jaeger, or Zipkin for end-to-end tracing across microservices and network components.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

The Monolith Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. As a forward deployed function, we partner closely with Fleet, Infrastructure, and AI Platform teams to embed data science directly into production environments—improving cluster reliability, increasing effective utilization (MFU), reducing MTTR, and protecting uptime and revenue.

About the role:

As a Forward Deployed Data Scientist, you will work at the intersection of data science and production systems, deploying advanced statistical models and machine learning methodologies directly within operational environments. You will collaborate closely with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system efficiency in real time. You will design experiments, analyze large-scale system telemetry data, and build predictive and optimization solutions that are tightly integrated into production workflows. This role blends hands-on deployment with analytical rigor, turning complex infrastructure data into measurable improvements in performance and cost. You will translate research and modeling insights into scalable, production-ready systems.

Who You Are:

• MS or PhD in Computer Science, Statistics, Applied Mathematics, Machine Learning, or related quantitative field
• 8+ years (or equivalent experience) applying statistical modeling or machine learning to large-scale datasets
• Strong proficiency in Python and scientific computing libraries (NumPy, pandas, SciPy, scikit-learn, PyTorch or TensorFlow)
• Demonstrated experience designing and analyzing controlled experiments (A/B testing, causal inference, hypothesis testing)
• Experience working with distributed data systems (Spark, Ray, Dask, or similar)
• Proficiency in SQL and working with large-scale structured datasets
• Experience building, deploying, and maintaining predictive models in production environments
• Strong understanding of optimization techniques (linear programming, convex optimization, stochastic optimization, or reinforcement learning)
• Experience working with time-series data and performance telemetry
• Ability to translate analytical insights into production-ready systems and workflows
• Strong collaboration skills and ability to work cross-functionally in fast-paced environments

Preferred:

• PhD with published research in systems optimization, distributed computing, ML systems, or performance modeling
• Experience with GPU workloads, distributed training, or AI infrastructure
• Familiarity with Kubernetes, containerized workloads, or cloud-native systems
• Experience deploying reinforcement learning or adaptive scheduling systems in production
• Background in capacity planning, forecasting, or resource allocation modeling
• Contributions to open-source ML or systems projects

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams—even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love uncovering hidden failure patterns in massive, noisy infrastructure datasets
• You enjoy working directly with engineering teams to deploy solutions that have immediate real-world impact
• You’re curious about building autonomous, agentic systems that investigate and explain system behavior
• You’re an expert in reinforcement learning, predictive modeling, or large-scale data analysis

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The starting salary will be determined by job-related knowledge, skills, experience, and the market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do

We’re seeking a talented and experienced Senior Engineer for Network Observability to join our Network Observability team. In this role, you will be a key player in designing, developing, and maintaining the monitoring, telemetry, and observability systems that keep CoreWeave’s GPU cloud network operating reliably and at scale. You’ll focus on building solutions that provide real-time insights into network performance, ensuring that issues are detected proactively and resolved quickly.

Your mission? To empower CoreWeave’s network with advanced observability: robust metrics, powerful analytics, and automated alerting—so well-tuned that any anomalies become clear before they ever impact our customers.

• Develop, optimize, and maintain network observability platforms. Use your skills in Python and Golang to create and automate collectors, exporters, and dashboards that provide deep visibility into network health and performance.
• Collaborate with Network Engineering and Platform teams to ingest and unify logs, metrics, and events from a variety of platforms (Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, SR Linux, etc.) into a single observability pipeline.
• Design and implement scalable telemetry solutions using protocols like gNMI, SNMP, and streaming analytics. Ensure advanced alerting and anomaly detection with tools such as Prometheus, Grafana, and Alertmanager.
• Work closely with network developers, site reliability engineers, and security teams to integrate observability solutions across the broader infrastructure. Participate in design discussions, RFCs, and architectural decisions.
• Join a rotating on-call schedule to troubleshoot and resolve observability-related issues. Provide timely support to operations teams, quickly isolating and fixing problems when they arise.
• Guide junior team members, share best practices, and foster a culture of continuous learning and improvement within the observability domain.

Investing in our people is one of our top priorities, and we value candidates who can bring their diversified experiences to our teams. Here are some qualities we’ve found compatible with our team. We'd love to talk about whether this aligns with your experience and interests and what you’re excited to work on next.

Who You Are

Minimum Qualifications

• Deep familiarity with Prometheus, Grafana, Alertmanager, gNMI, and SNMP. Experience writing or extending custom metric collectors/exporters is a plus.
• Experience as a Network Engineer, SRE, Software Developer, or Systems Administrator in large-scale environments. A track record of building and operating robust telemetry and monitoring solutions is a plus.
• Passion for automating tasks and processes. You find satisfaction in creating workflows that handle repetitive tasks and reduce human error to near zero.
• Comfortable containerizing solutions in Kubernetes, designing, building, and deploying container-based workloads efficiently.
• Proficient with Python, Go, and Bash, plus familiarity with configuration management and templating tools (e.g., Ansible, Jinja2). .
• Strong knowledge of Linux systems and IP networking concepts, with hands-on experience in routing, switching, and network troubleshooting.
• Practical knowledge with a variety of platforms, including Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, and SR Linux.
• Collaborative, humble, and always ready to help others while staying open to learning from more senior colleagues.

Preferred Qualifications

• College Education: Bachelor’s degree in Computer Science or a related field.
• Machine Learning for Anomaly Detection: Hands-on experience applying ML techniques or tools (e.g., TensorFlow, scikit-learn) to proactively detect performance or security anomalies in network traffic.
• Network Certifications: Certifications like CCNA, CCNP, or similar.
• Advanced Metrics & Analytics: Hands-on experience with data pipelines, event correlation, or anomaly detection in large-scale environments.
• Distributed Tracing: Familiarity with OpenTelemetry, Jaeger, or Zipkin for end-to-end tracing across microservices and network components.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

To fulfil our obligation to protect client data, successful applicants offered employment with CoreWeave will be required to complete a basic criminal record check, conducted in compliance with GDPR. Employment offers are conditional upon receiving satisfactory check results

SLSQ427R253

As a Partner Solutions Architect for Europe, you will work with the technical and sales team members who work directly with our customers. You will develop 'technical champions’ within our top Partners, providing enablement on technical matters related to the Databricks product. Working with our partners, you will help our customers achieve tangible, data-driven outcomes through our Databricks Data Intelligence Platform, enabling data teams to complete projects and integrate them into their enterprise Ecosystem. As a member of our team, you will exercise and develop expertise in those areas, using open source projects such as Apache Spark™, MLflow, and Delta Lake, and major public cloud infrastructure and services. the product. You will use this expertise to become a trusted advisor to C&SI partners.

The impact you will have:

• Provide partners with the level of enablement they need to assist their clients in evaluating and adopting Databricks, including hands-on Apache Spark™ programming and integration with the wider cloud ecosystem.

• Engage with the partner technical community by leading workshops, seminars, and meet-ups.

• You will be a big data analytics expert in architecture and design, and will share this knowledge with our partner network.

• Show expertise by producing creative technical solutions and blog posts.

What we look for:

• 8 years of customer-facing experience working with external clients or partners across a variety of industry markets

• Core strength in either data engineering or data science

• Experience demonstrating technical concepts, including presenting and whiteboarding

• Experience developing architectures within a public cloud (AWS, Azure, or GCP)

• Hands-on experience in SQL, Python, Scala, or Java

• Expertise in at least one of the following:

• Data Engineering technologies (Ex, Apache Spark™, Hadoop, Kafka)

• Data Warehousing (Ex, SQL, OLTP/OLAP/DSS)

• Data Science and Machine Learning technologies (Ex, pandas, scikit-learn, HPO)

We’re looking for a talented Senior Research Data Scientist who expects more from their career. It’s a chance to extend and improve dunnhumby’s world class science capabilities. It’s an opportunity to work with a market-leading business to explore new opportunities for us and influence global retailers and suppliers.

Joining our team, you’ll work with world class and passionate people to apply machine learning and statistical techniques to business problems. You’ll contribute to the research and implementation of new approaches to address complex problems and perform data analysis and model validation. You’ll have the opportunity to present results to a variety of internal stakeholders and will apply these techniques and algorithms to create dunnhumby science solutions that can be delivered across our clients and engineered into science modules.

This role will be focussed across insight automation and product assortment, with applications of science including identifying new and growing product needs, optimising the mix of products, predicting the impact of changes, product attribute generation and applications of generative AI for product support and insights.

What you'll be doing:

• Create new science-based solutions that can be captured as science modules and applied across clients, with support from senior team members.
• Pick up new machine learning approaches, such as regularised regression, clustering or tree-based ensembles, graph-based approaches, natural language processing and neural network techniques and apply them on client data.
• Perform exploratory data analysis to characterise and visualise datasets 
• Extend and develop programming skills, in languages such as Python and Spark, to develop efficient science code for science modules.
• Help identify new opportunities within the Data Science space for future dunnhumby solutions.
• Implement advice from colleagues to resolve challenges.
• Follow Quality Assurance processes, ways of working and meet coding standards.
• Ensure smooth running of your projects, working with senior team members for direction.
• Build strong relationships within the team and with internal stakeholders, ensuring clear and effective communication.

Who you’ll get to work with:

• Within dunnhumby you’ll primarily work with
• Applied and Research Data Scientist teams
• Data Science Engineering teams
• Product and Client teams where required

What you'll need:

• Master’s degree or equivalent in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
• Experience with machine learning techniques such as regularised regression, clustering or tree-based ensembles, and the ability to implement them through libraries.
• Experience with programming, ideally Python, and the ability to quickly pick up handling large data volumes with modern data processing tools, e.g. by using Hadoop / Spark / SQL
• Experience with or ability to quickly learn open-source software including machine learning packages, such as Pandas and scikit-learn, along with data visualisation technologies.
• A willingness to present your work to both technical and non-technical audience and to contribute to the wider data science community.

A plus if you also have:

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
• Experience in retail sector.

We’re looking for a talented Research Data Scientist who expects more from their career. It’s a chance to extend and improve dunnhumby’s world class science capabilities. It’s an opportunity to work with a market-leading business to explore new opportunities for us and influence global retailers and suppliers.

Joining our team, you’ll work with world class and passionate people to apply machine learning and statistical techniques to business problems. You’ll contribute to the research and implementation of new approaches to address complex problems and perform data analysis and model validation. You’ll have the opportunity to present results to a variety of internal stakeholders and will apply these techniques and algorithms to create dunnhumby science solutions that can be delivered across our clients and engineered into science modules.

This role will be focussed across insight automation and product assortment, with applications of science including identifying new and growing product needs, optimising the mix of products, predicting the impact of changes, product attribute generation and applications of generative AI for product support and insights.

What you'll be doing:

• Create new science-based solutions that can be captured as science modules and applied across clients, with support from senior team members.
• Pick up new machine learning approaches, such as regularised regression, clustering or tree-based ensembles, graph-based approaches, natural language processing and neural network techniques and apply them on client data.
• Perform exploratory data analysis to characterise and visualise datasets 
• Extend and develop programming skills, in languages such as Python and Spark, to develop efficient science code for science modules.
• Help identify new opportunities within the Data Science space for future dunnhumby solutions.
• Implement advice from colleagues to resolve challenges.
• Follow Quality Assurance processes, ways of working and meet coding standards.
• Ensure smooth running of your projects, working with senior team members for direction.
• Build strong relationships within the team and with internal stakeholders, ensuring clear and effective communication.

Who you’ll get to work with:

• Within dunnhumby you’ll primarily work with
• Applied and Research Data Scientist teams
• Data Science Engineering teams
• Product and Client teams where required

What you'll need:

• Master’s degree or equivalent in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
• Experience with machine learning techniques such as regularised regression, clustering or tree-based ensembles, and the ability to implement them through libraries.
• Experience with programming, ideally Python, and the ability to quickly pick up handling large data volumes with modern data processing tools, e.g. by using Hadoop / Spark / SQL
• Experience with or ability to quickly learn open-source software including machine learning packages, such as Pandas and scikit-learn, along with data visualisation technologies.
• A willingness to present your work to both technical and non-technical audience and to contribute to the wider data science community.

A plus if you also have:

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
• Experience in retail sector.

Purpose of role:

To generate powerful execution insights for a wide range of internal stakeholders (including PMs, traders, senior management and sales) and conduct research into algorithm and signal development to understand and optimise trading dynamics and maximise the realised P&L of strategies.

Specific responsibilities:

• Leverage the Execution Analytics platform to generate powerful execution insights for PMs and traders, helping to reduce market impact, improve capacity and inform optimal trading strategies.
• Research and prototype new analytics and tools to improve the Execution Analytics platform and Quant Toolkit.
• Model transaction costs, market impact and order fill rates across asset classes using advanced statistical and ML techniques.
• Analyse large datasets to formulate mathematical models of microstructure patterns, order book dynamics and intraday predictive signals.
• Drive automation, scalability and standardisation of analytics across experimentation, algo performance measurement and execution oversight.
• Partner with technologists to deploy and optimise analytics, ensuring the Execution Analytics platform remains accurate, stable and operationally robust.
• Act as a subject matter expert on execution data, supporting colleagues, sales and senior stakeholders across the firm.

Technical competencies:

• PhD or exceptional Masters qualification in a quantitative subject.
• 6-12 Months post graduate work experience.
• Experience of Buy side / Sell side, essential.
• Ability to think creatively, develop and implement original research and ideas.
• Exceptional analytical skills; ability to deliver end-to-end.
• A solid understanding of statistics and an ability to apply it to real world problems.
• Expertise in Python and related Data Science/ML libraries such as Pandas, Scikit Learn, Tensorflow.
• Experience of handling large data sets and tick data.
• Experience with visualisation tools and libraries.
• Understanding of financial products and market microstructure.

Why Engineering at Dataiku?

Dataiku’s on-premise, cloud, or SaaS-deployed platform connects many data science technologies, and our technology stack reflects our commitment to quality and innovation. We integrate the best of data and AI tech, selecting tools that truly enhance our product. From the latest LLMs to our dedication to open source communities, you'll work with a dynamic range of technologies and contribute to the collective knowledge of global tech innovators. You can find out even more about working in Engineering at Dataiku by taking a look here.

Here are some useful links so you preview what we do at Dataiku: Dataiku's Key Capabilities ; Dataiku's Github, you can also take a look at the Gallery, a public instance showcasing some example projects (note editing is very limited and will be regularly reset). 

Our product is called Dataiku DSS which stands for Dataiku Data Science Studio. If you’d like to know more about it, you can watch the demo here or try the free version here.

How you’ll make an impact

This position is either onsite/hybrid from our London office or full remote from any part of the UK.

As a Fullstack Engineer, you’ll contribute to building Dataiku DSS core features by joining one of the following themes:

• Data Preparation: Develop tools for data integration, transformation, and cataloging, as well as Jupyter notebooks, SQL workbenches, and APIs.
  • Add new capabilities for data integration, transformation, and visualization using LLMs
  • Optimize workflows for large-scale datasets and improve database support
  • Strengthen developer tools like Jupyter notebooks and SQL workbenches
  • Tech stack: Java, Python, AngularJS, Angular

• AI & Machine Learning: Collaborate on next-gen AI features, from statistics and time series forecasting to LLM inference.
• Build next-generation features like cross-provider LLM APIs or integration with the latest Machine Learning models
• Collaborate with research teams on innovative machine learning experiments
• Tech stack: Python, scikit-learn, TensorFlow, PyTorch, Java, Angular, AngularJS

• Data Consumption: build the experience that makes data accessible and actionable for everyone.
  • Enhance our collaborative Workspaces to organize and explore charts, dashboards, datasets and more
  • Create innovative interfaces that answer business questions and surface relevant insights using LLMs
  • Tech stack: Angular, AngularJS, Java, Python

• Data Visualization: elevate our data visualization capabilities with high-performance charting tools and dashboards.
  • Develop new chart types and optimize visualization performance
  • Enhance dash boarding capabilities to deliver fast and flexible experiences
  • Tech stack: React, AngularJS, Angular, D3.js, ECharts, Node.js, Java, Spring

• MLOps: Develop back-end capabilities for automating and monitoring ML model lifecycles while supporting cross-functional collaboration.
  • Build tools to automate retraining, monitoring, and deployment of ML models
  • Enhance collaboration features for ML stakeholders
  • Tech stack: Python, Java, Angular, Angular.JS, Kubernetes, Docker
    
    
• Platform: Focus on scaling and securing the platform while improving cloud integrations and supporting a wide array of data sources and engines.
  • Optimize processing engines for scalability and latency
  • Expand capabilities to support new databases and cloud platforms
  • Tech stack: Java, Python, Angular, Kubernetes, Spark, AWS, Azure, GCP
    
    
• AI Governance: Build features for AI compliance, connecting disparate systems and simplifying governance processes.
  • Develop customizable platforms for managing AI compliance and governance
  • Simplify policy enforcement and integration across disparate systems
  • Tech stack: Java, Angular, PostgreSQL

What you need to be successful 

• We’re stack agnostic, so all you need is a significant experience in software engineering,  building a real product
• We’re hiring at all levels (junior, mid, senior)
• A passion for combining backend performance with exceptional frontend user experience.
• You want to work in a fast-paced, high-growth environment that values diversity of talent, excellence of product, and exciting engineering challenges at scale

What does the hiring process look like?

• Initial call with a member of our Technical Recruiting team (45min)
• Video call with an Engineering Team Lead (1h30)
• Technical Assessment to show your skills (Home Test or Live Coding)
• Final Interviews with our VPs of Engineering (2h)   #LI-Remote

As a Data Scientist supporting AI engineers, you will partner with one or more engineering teams, developing actionable insights that guide improvements to the Wayve AI Driver. Using experimental and observational analyses of real and simulated driving, you will help teams advance the functionality, safety, and performance of the Wayve AI Driver, helping to advance Wayve as the leader in end-to-end AI for autonomous mobility.

This means you might:

• Formulate and iterate upon the performance metrics that organize our engineering efforts and guide progress toward commercial success
• Design experiments and targeted off-road measurements to ensure that we deliver product requirements to customers while maintaining safety and performance
• Investigate factors in model training and inference leading to bottlenecks in functionality and performance, identifying and validating hypotheses for unlocking improvements

What we are looking for in our candidate

Essential

• 3+ years experience working in a Data Science role.
• Fluent in querying and building large datasets, writing production-level SQL for use in data-transformation pipelines.
• Prior experience designing robust real-world experiments (e.g. A/B) and critically evaluating test-statistics
• Foundations in the fundamentals behind statistics: testing appropriate distributions, testing the assumptions behind frequentist stats
• Proficient in using a statistical scripting language and data science/ML packages (e.g. python such as pandas, sklearn, statsmodels, scipy or R such as dplyr, caret, stats)
• Well-versed in summarising, visualising and communicating findings in an accessible and compelling way
• Track record of influencing team direction through your findings  
• A bias towards deriving actionable insight that can be used to drive prioritisation and strategy for others.
• Comfortable working asynchronously across time zones with cross-functional partners
• You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.

Desired

• Practical experience with machine learning (e.g. pytorch). Passion to take research ideas to production.
• Track record of promoting statistical rigour and experimental best practices in your prior roles.
• Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing.
• Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)
• Experience working in a fast-moving tech company or startup.

As a Data Scientist supporting AI engineers, you will partner with one or more engineering teams, developing actionable insights that guide improvements to the Wayve AI Driver. Using experimental and observational analyses of real and simulated driving, you will help teams advance the functionality, safety, and performance of the Wayve AI Driver, helping to advance Wayve as the leader in end-to-end AI for autonomous mobility.

This means you might:

• Formulate and iterate upon the performance metrics that organize our engineering efforts and guide progress toward commercial success
• Design experiments and targeted off-road measurements to ensure that we deliver product requirements to customers while maintaining safety and performance
• Investigate factors in model training and inference leading to bottlenecks in functionality and performance, identifying and validating hypotheses for unlocking improvements

What we are looking for in our candidate

Essential

• 3+ years experience working in a Data Science role.
• Fluent in querying and building large datasets, writing production-level SQL for use in data-transformation pipelines.
• Prior experience designing robust real-world experiments (e.g. A/B) and critically evaluating test-statistics
• Foundations in the fundamentals behind statistics: testing appropriate distributions, testing the assumptions behind frequentist stats
• Proficient in using a statistical scripting language and data science/ML packages (e.g. python such as pandas, sklearn, statsmodels, scipy or R such as dplyr, caret, stats)
• Well-versed in summarising, visualising and communicating findings in an accessible and compelling way
• Track record of influencing team direction through your findings  
• A bias towards deriving actionable insight that can be used to drive prioritisation and strategy for others.
• Comfortable working asynchronously across time zones with cross-functional partners
• You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.

Desired

• Practical experience with machine learning (e.g. pytorch). Passion to take research ideas to production.
• Track record of promoting statistical rigour and experimental best practices in your prior roles.
• Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing.
• Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)
• Experience working in a fast-moving tech company or startup.

The Role

As a Quant Developer at Man Group you will be part of one of our flagship teams in Man’s systematic trading arm - Man AHL. The team works hand-in-hand with Quant Researchers focusing on equity market neutral models. Your challenges will be varied, and will involve building new alphas and new trading strategies.

The Team

Quant Developers at Man Group are all part of our broader technology team, members of a group of over sixty individuals representing eighteen nationalities. We have varied backgrounds including Computer Science, Mathematics, Physics, Engineering – even Classics - but what unifies us is a passion for technology and writing high-quality code.

Our developers are organised into small cross-functional teams, with our engineering roles broadly of two kinds: “Quant Platform Developers” otherwise known as our “Core Techs”, and “Quant Developers” which we often refer to as “Sector Techs”. We use the term “Sector Tech” because some of our teams are aligned with a particular asset class or market sector. People often rotate teams in order to learn more about our system, as well as find the position that best matches their interests.

 
Our Technology

Our systems are almost all running on Linux and most of our code is in Python, with the full scientific stack: numpy, scipy, pandas, scikit-learn to name a few of the open-source libraries we use extensively. We implement the systems that require the highest data throughput in Java and C++.

We use Airflow for workflow management, Kafka for data pipelines, Bitbucket for source control, Jenkins for continuous integration, Grafana + Prometheus for metrics collection, ELK for log shipping and monitoring, Docker and Kubernetes for containerisation, OpenStack for our private cloud, Ansible and Terraform for architecture automation, and Slack for internal communication. We heavily utilise ArcticDB (https://arcticdb.io/) our in-house developed DataFrame Database. Our technology list is never static: we constantly evaluate new tools and libraries.

 
Technology and Business Skills

At AHL we strive to hire only the brightest and best and most highly skilled and passionate technologists.

Essential

• Exceptional technology skills; recognised by your peers as an expert in your domain
• A keen interest and understanding of financial markets and instruments
• A proponent of strong collaborative software engineering techniques and methods: agile development, continuous integration, code review, unit testing, refactoring and related approaches
• Strong knowledge of Python
• Proficient on Linux platforms with knowledge of various scripting languages
• Experience of data analysis techniques along with relevant libraries e.g. NumPy/SciPy/Pandas
• Relevant mathematical knowledge e.g. statistics, asset pricing theory, optimisation algorithms

Advantageous

• Experience of front office quantitative software development e.g. in a hedge fund or investment bank
• Experience of web based development and visualisation technology for portraying large and complex data sets and relationships
• Experience of machine learning techniques, natural language processing and related libraries and frameworks e.g. scikit-learn, Tensorflow

 
Personal Attributes

• Strong academic record and a degree with high mathematical and computing content e.g. Computer Science, Mathematics, Engineering or Physics from a leading university
• Craftsman-like approach to building software; takes pride in engineering excellence and instils these values in others
• Demonstrable passion for technology e.g. personal projects, open-source involvement
• Intellectually robust with a keenly analytic approach to problem solving
• Self-organised with the ability to effectively manage time across multiple projects and with competing business demands and priorities
• Focused on delivering value to the business with relentless efforts to improve process
• Strong interpersonal skills; able to establish and maintain a close working relationship with quantitative researchers, traders and senior business people alike
• Confident communicator; able to argue a point concisely and deal positively with conflicting views

 
Working Here

Man Group has a small company, no-attitude feel. It is flat structured, open, transparent and collaborative, and you will have plenty of opportunity to grow and have enormous impact on what we do.  We are actively engaged with the broader technology community.

• We host and sponsor London’s PyData and Machine Learning Meetups
• We open-source some of our technology. See github.com/man-group
• We regularly talk at leading industry conferences, and tweet about relevant technology and how we’re using it. See @ManQuantTech

We’re fortunate enough to have a fantastic open-plan office overlooking the River Thames, and continually strive to make our environment a great place in which to work. We believe that agile working allows us to deliver the best business outcomes for our clients and investors as well as having a positive impact on life / work balance and the wellbeing of our staff. We are simultaneously committed to "levelling the playing field" and believe that agile working promotes inclusivity across teams, regions and business units.

• We organise regular social events, everything from photography through climbing, karting, wine tasting and monthly team lunches
• We have annual away days and off-sites for the whole team
• As well as PC’s and Macs, in our office you’ll also find numerous pieces of cool tech such as light cubes and 3D printers, guitars, ping-pong and table-football, and a piano

We offer competitive compensation, a generous holiday allowance, various health and other flexible benefits. We are also committed to continuous learning and development via coaching, mentoring, regular conference attendance and sponsoring academic and professional qualifications.

Miro is looking for a Machine Learning Research Engineer to serve as the technical "North Star" for our Machine Learning organization. You will operate as an Individual Contributor, driving the architectural decisions behind the "Intelligent Canvas."

Your challenge is unique in the industry: You are not just processing text. You are building models that understand spatial relationships, visual diagrams, and unstructured collaboration. You will research, prototype, and ship novel architectures that combine Large Language Models (LLMs), Computer Vision, and Graph Neural Networks (GNNs) to make Miro the smartest collaboration platform on earth.

What you’ll do

• Design, train, and ship production-grade ML models—including deep learning, NLP, and computer vision systems—that solve complex business problems and power core product features.
• Conduct deep exploratory research on massive datasets to uncover novel patterns in user behavior and content creation, translating raw data insights into new predictive modeling opportunities.
• Apply advanced fine-tuning strategies (e.g., PEFT, LoRA) to adapt state-of-the-art foundation models to specific domain tasks, rigorously experimenting to maximize performance.
• Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation, ensuring high data quality and system reliability.
• Optimize model performance for latency, throughput, and resource utilization, balancing model complexity with production constraints (e.g., overfitting vs. underfitting, compute efficiency).
• Collaborate cross-functionally with data engineers, product managers, and software engineers to translate business requirements into technical ML specifications and integrate models into user-facing applications44.
• Champion MLOps excellence by automating deployment workflows, implementing CI/CD for ML, and establishing robust monitoring for model drift and health.
• Stay at the forefront of ML research, evaluating novel algorithms and techniques (e.g., Transformer architectures, quantization) to drive innovation and technical strategy.

What you’ll need

• Strong foundation in ML theory and statistics, including hypothesis testing, probability distributions, regression, classification, and optimization techniques.
• Solid engineering fundamentals. You are comfortable writing production-level Python and have a deep understanding of data structures, algorithms, and distributed system design.
• Deep proficiency in Python and the modern ML stack, with hands-on experience using libraries like Pandas, NumPy, Scikit-learn, and deep learning frameworks (PyTorch, TensorFlow).
• Gradient Debugging: Expertise in PyTorch or JAX, including experience with distributed training (e.g., DDP, FSDP) and debugging complex gradient issues.
• Applied Research: Ability to read, implement, and improve upon the latest academic papers (NeurIPS, ICML, CVPR). You don't just use libraries; you understand the math underneath them and can reproduce results in peer-reviewed papers.
• Track record of end-to-end ML delivery, from exploratory data analysis (EDA) and feature engineering to training, validation, and deploying models in a production environment.
• Experience with large-scale systems, capable of designing resilient architectures that handle vast datasets and high-throughput inference requests.
• Strong engineering mindset, valuing code quality, testing, modularity, and maintainability just as highly as model accuracy.

Education + Experience

• Option A: Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related field plus ~3+ years of professional ML engineering experience.
• Option B: No formal degree, ~6+ years of industry experience demonstrating equivalent proficiency in building and shipping ML systems.

What's in it for you

LON:

• Competitive equity package
• Health insurance for you and your family
• Corporate pension plan
• Lunch, snacks and drinks provided in the office
• Wellbeing benefit and WFH equipment allowance
• Annual learning and development allowance to grow your skills and career
• Opportunity to work for a globally diverse team
  

Multi Location: Amsterdam / Berlin / Yerevan / London:

• Competitive equity package
• Lunch, snacks and drinks provided in the office
• Wellbeing benefit and WFH equipment allowance
• Annual learning and development allowance to grow your skills and career
• Opportunity to work for a globally diverse team

Recruiter: #LI-MH1

AI Engineer - FDE (Forward Deployed Engineer) (ALL LEVELS)

Req ID: CSQ426R186

Mission

The AI Forward Deployed Engineering (AI FDE) team is a highly specialised customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specialisations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly.

The impact you will have:

• Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI research to solve customer problems
• Own production rollouts of consumer and internally facing GenAI applications
• Serve as a trusted technical advisor to customers across a variety of domains
• Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally
• Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap 

What we look for:

• Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy
• Expertise in deploying production-grade GenAI applications, including evaluation and optimizations 
• Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving business value through AI
• [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets

ABOUT CUBIST

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.

ROLE/RESPONSIBILITIES

• Perform rigorous and innovative research to discover systematic anomalies in global macro markets (futures, FX, etc.)
• Perform feature engineering with price-volume, order book and alternative data at intraday to daily horizons in mid frequency trading space
• Perform feature combination and monetization using various modeling techniques
• Manage the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation
• Maintain and improve portfolio trading in a production environment
• Contribute to the analysis framework for scalable research

REQUIREMENTS

• Background in mathematics, statistics, machine learning, computer science, engineering, quantitative finance, or economics
• 2-6 years of signal research experience in macro trading as part of a trading team
• Specialization in swaps, fixed income, or commodities trading a plus.
• Prior professional experience with feature engineering, modeling, or monetization
• Ability to efficiently format and manipulate large, raw data sources
• Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas
• Strong command of foundations of applied and theoretical statistics, linear algebra, and machine learning techniques
• Collaborative mindset with strong independent research abilities
• Commitment to the highest ethical standards

1211 - Director, Data Science - DTA

About WPP Media

WPP is the creative transformation company. We use the power of creativity to build better futures for our people, planet, clients and communities. For more information, visit wpp.com.

WPP Media is WPP’s global media collective. In a world where media is everywhere and in everything, we bring the best platform, people, and partners together to create limitless opportunities for growth. For more information, visit wppmedia.com

About Choreograph: A Leading WPP Media Brand

Choreograph is WPP’s global data products and technology company. We’re on a mission to transform marketing by building the fastest, most connected data platform that bridges marketing strategy to scaled activation.

We work with agencies and clients to transform the value of data by bringing together technology, data and analytics capabilities. We deliver this through the Open Media Studio, an AI-enabled media and data platform for the next era of advertising.

We’re endlessly curious. Our team of thinkers, builders, creators and problem solvers are over 1,000 strong, across 20 markets around the world.

Role Summary and Impact

The Data Science Director, plays a critical leadership role as part of a broader team in the deployment and development of predictive models for our customers. You will lead and mentor a team, working collaboratively with senior Google Cloud and Google Marketing experts to apply advanced modeling techniques to enhance our customers' media/marketing solutions. As Team Lead you will be responsible for guiding the team in building out industry-relevant predictive models that can be applied to optimize our customers' media investments.

The role focuses on leading the application of analytical solutions based on machine learning & statistical predictive algorithms on top of the Google technology stack and agent development for our customers. We are looking for a Team Lead with significant experience in algorithmic data analysis, proven scientific research skills, and a track record of leading successful data science initiatives.

Skills and Experience

At WPP Media, we believe in the power of our culture and our people. It’s what elevates us to deliver exceptional experiences for both our clients and each other. In this role it will be critical to embrace WPP & WPP Media’s shared core values:

• Be Extraordinary by Leading Collectively to Inspire transformational Creativity.
• Create an Open environment by Balancing People and Client Experiences by Cultivating Trust.
• Lead Optimistically by Championing Growth and Development to Mobilize the Enterprise.

This is an excellent opportunity for a Team Lead, to join the Data and Technology Consulting Team. You will be instrumental in strategising how to tackle and take apart large and messy datasets of structured and unstructured data, oversee the building and implementation of models and algorithms, agents and design and run experiments to optimise clients customer insights, analytics and customer experience. The ideal candidate will have a love of getting their hands dirty, while also empowering and developing their team to do the same.

RESPONSIBILITIES:

• Lead the team in the creation, management, and continuous improvement of AI/ML and models and agents used to measure, optimize, and activate business transformation, including creative, media campaigns and broader marketing objectives.
• Drive the development and deployment of industry-relevant strategies and solutions.
• Ensure comprehensive documentation of data science projects, establishing best practices for replicable and scalable analytical processes across clients.
• Guide the team in building out compelling content for presentations to demonstrate the performance and value of statistical and predictive models.
• Oversee the delivery of robust mathematical analysis, ensuring accuracy and strategic relevance.
• Accountable for the team's production of high-standard campaign reports, studies, presentations, and proposals.
• Achieve continuous improvement by proactively assessing Choreograph's working relationships, practices, and methods, and implementing enhancements.
• Lead the establishment and optimization of MLOps processes leveraging the GCP toolset.
• Drive and demonstrate measurable success and impact for clients and the team.

REQUIREMENTS:

• Hands-on experience with building out machine learning models with Python, in a leadership or senior mentorship role.
• Advanced education and background in machine learning, computer science, physics, applied mathematics, or statistics.
• Expert-level hands-on experience in developing complex query structures with SQL and/or NoSQL query languages, and experience guiding others.
• Expert-level hands-on experience working with GCP’s AI Capabilities, including leveraging BigQuery ML, Vertex AI, and other relevant services.
• Extensive hands-on experience with a broad range of machine learning techniques using (TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, Pandas, NLTK, Spark MLib).
• Deep experience and understanding of deep learning techniques (TensorFlow, Theano, MXNet).
• Proven understanding and experience in software engineering best practices, DevOps, and MLOps, with a track record of implementing or leading these initiatives.
• Solid grounding in applied mathematics, especially optimization and graph theory, and advanced statistics.
• Significant commercial or academic experience in leading the development and deployment of complex ML & AI solutions using Python/R.
• In-depth knowledge of machine learning algorithms and their practical applications.
• Advantageous to have extensive experience in Google Marketing Platform and the use of Ads Data Hub.
• Exceptional capabilities in the areas of data engineering, data modeling, and complex big data analysis, with experience leading teams in these domains.
• Strong business analysis skills and deep understanding of analytics, with the ability to translate complex data into strategic business value.
• Exceptional communication and presentation skills, capable of influencing stakeholders at all levels.
• Passionate about working in an extremely fast-paced, demanding, and fluid environment, with a desire to build and lead a high-performing team and contribute significantly to a growing company.

Life at WPP Media & Benefits

Our passion for shaping the next era of media includes investing in our employees to help them do their best work, and we’re just as committed to employee growth as we are to responsible media investment. WPP Media employees can tap into the global WPP Media & WPP networks to pursue their passions, grow their networks, and learn at the cutting edge of marketing and advertising. We have a variety of employee resource groups and host frequent in-office events showcasing team wins, sharing thought leadership, and celebrating holidays and milestone events. Our benefits include competitive medical, group retirement plans, vision, and dental insurance, significant paid time off, preferential partner discounts, and employee mental health awareness days.  

WPP Media is an equal opportunity employer and considers applicants for all positions without discrimination or regard to particular characteristics. We are committed to fostering a culture of respect in which everyone feels they belong and has the same opportunities to progress in their careers.

We believe the best work happens when we're together, fostering creativity, collaboration, and connection. That's why we’ve adopted a hybrid approach, with teams in the office around four days a week. If you require accommodations or flexibility, please discuss this with the hiring team during the interview process.

Please note this is a UK based role and requires individuals to have the right to work in this location

Please read our Privacy Notice (https://www.wppmedia.com/pages/privacy-policy) for more information on how we process the information you provide.

#LI-Promoted