• Design and implement production-grade auto-labeling pipelines that generate 3D and 4D annotations (objects, trajectories, maps) from multi-modal robot data at scale. • Develop data-centric learning workflows that connect auto-label outputs, Serve’s dataset infrastructure, and continuous E2E model training and evaluation pipelines. • Lead initiatives in self-training, weak supervision, and simulation-to-real adaptation to reduce manual labeling dependency and accelerate model iteration cycles. • Collaborate cross-functionally with multiple autonomy teams(e.g ML-infra, mapping, simulation) to align labeling infrastructure with model training and evaluation workflows. • Stay ahead of emerging trends in E2E autonomy and data-centric ML, identifying opportunities to productionize state-of-the-art techniques. • Mentor and support ML engineers and interns in developing robust data-centric practices, from dataset curation and labeling feedback loops to model monitoring and continuous improvement.

• Deployment of ML pipelines in production • Improving and researching algorithms for Video2Haptic generation • Creating algorithms for video metadata extraction and action recognition • Improvement of the algorithms for high-resolution video matting • Improvement of the algorithms of content censuring • Research and development of algorithms for spatiotemporal ROI extraction in videos for short clips and trailer creation • Research and development into the field of volumetric video generation and streaming

• Explore data and real-world cases to develop signals and machine learning models that identify and reduce platform risk. • Partner with cross-functional teams to design practical ML solutions that fit real workflows. • Analyze and prepare training data, including working with crowdsourced labeling and human-in-the-loop processes. • Prototype, train, and iterate on machine learning models, using a mix of established and novel techniques. • Own the path from idea to production: deploy ML and signal-based models, and write backend code when needed. • Build observability into models, debug performance gaps, and continuously improve based on real-world results. • Measure impact using offline evaluation and experiments such as A/B tests.

Company Description Hi! We're UW. We’re on a mission to take the headache out of utilities by providing them all in one place. One bill for energy, broadband, mobile and insurance and a whole lot of savings! We’re aiming to double in size as we help more people to stop wasting time and money. Big ambitions, to be delivered by people like you. Got your attention? Read on.. We put people first.. it's all about you! We are seeking a production-focused Machine Learning Engineer to bridge the gap between data science research and scalable, reliable software. In this role, you will partner with Data Scientists to re-architect experimental models (POCs)—such as Next Best Action and Churn Propensity—for production. You will own "Day 2" operations including deployment, latency optimization, and monitoring, while also building the infrastructure for GenAI and RAG applications powering our tools. Job Description We Deliver Progress.. What you'll do and how you'll make an impact.. As a Machine Learning Engineer at UW, your responsibilities will include: - Predictive Modelling: Design and deploy robust ML models to solve business challenges, specifically Churn Propensity and Next Best Action (NBA) engines. - Customer Analytics: Develop advanced Customer Segmentation using clustering techniques to tailor services and communications. - Commercial Valuation: Own xLTV and ROI logic, modeling long-term customer value to optimize acquisition and retention spend. - Deployment & Ops: Collaborate with Data Engineers to productionise scalable models, ensuring continuous monitoring for drift and performance. - Experimentation: Design and analyse A/B tests to validate model effectiveness and measure commercial uplift. - Stakeholder Partnership: Translate complex statistical outputs into actionable insights for Marketing, Product, Commercial and Ops stakeholders. Qualifications We put people first. It’s all about you.. - Technical Mastery: - Production ML Experience: Proven experience deploying Machine Learning models into high-traffic production environments (retail, fintech, or utilities experience is a plus). - Tech Stack: Strong proficiency in Python and software engineering best practices (unit testing, modular code, Git). Experience with containerization (Docker, Kubernetes) is essential. - MLOps Tooling: Experience with model registries and monitoring tools (e.g., MLflow, Grafana). - Desirables: Experience with Feature Stores (e.g., Feast, Tecton). Knowledge of streaming data technologies (Kafka, Pyspark). Hands-on experience building or deploying LLM-based applications, specifically working with RAG architectures and vector databases. - Impact & Scope: - You have a track record of leading high-impact initiatives that align with company strategy. You can evaluate proposed work against team goals and provide critical feedback to ensure value delivery. - Planning & Delivery: - You are capable of independently implementing small to medium sized features through to completion. - Operational Excellence: - Continuous improvement mindset: Identify process gaps and proactively propose solutions, seeking out feedback from your team. - Business & Domain Knowledge: - Experience in working in a relevant consumer-centric domain - Can advise stakeholders on how Machine Learning Engineering can be applied to solve business problems - Leadership & Culture: - Collaboration: A "Software Engineering mindset" with the ability to work empathetically with Data Scientists, understanding their workflows while enforcing production standards. Skills / Competencies - Strategic Problem Solving: Ability to break down vague, high-level business requirements into concrete, scalable technical architectures. - Clear Communication: Excellent verbal and written skills, with the ability to influence technical and non-technical audiences. - Accountability: Willingness to take ownership of critical systems and participate in on-call rotations. - Continuous Learning: Proactively seeking out the latest industry trends and introducing relevant innovations to the team. Don’t worry if you don’t have the whole list. If you feel you have most of it and can learn the rest pretty quickly then please don’t hesitate to apply. Overall we are looking for imaginative and pragmatic problem-solvers who want to help make a positive impact with data at UW. Additional Information We’ve got big ambitions so there’s going to be plenty of challenges. There are also a lot of benefits: - An industry benchmarked salary. We’ll share it during your first conversation. - Discretionary performance bonus - Flexible remote working with the option to visit our London hubs for team meetings and company events. You'll have access to a range of hot desking spaces around the UK - Electric Car salary sacrifice scheme through Tusker - Discount on our services and you get our coveted Cashback Card for free. You’ll also get access to 100s of rewards and discounts through Perkbox - A matched contribution pension scheme and life assurance up to 4x your salary. You can also access free mortgage advice and a financial wellbeing tool. - Family-friendly policies, designed to help you and your family thrive. - Discounted private health insurance, access to an Employee Assistance line and a free Virtual GP. Our wellbeing app Unmind supports your mental health. - Belonging groups that help UW shape an even more inclusive future. - A commitment to helping you develop your career journey through learning, coaching and new experiences Not sure you meet all the requirements? Let us decide! Research shows that women and members of other underrepresented groups tend not to apply for jobs if they think they may not meet every qualification, when in fact they often do. We provide equal opportunities, a diverse and inclusive work environment, and fairness for everyone. You are welcome to apply no matter your age, disability, gender, marriage or civil partnership status, pregnancy and maternity status, race, religion or belief, or sexual orientation. Please don’t be afraid to ask about what we can do to support your needs. All requests will be carefully and fairly considered. Please note, if you are successful and offered a role at UW, you will be subject to a background check. Where checks are unsatisfactory or incomplete and/or a failure to reveal information relating to convictions that you are required to identify as part of the background checks, could lead to withdrawal of an offer of employment. - Employment Status: Full-Time