• work as part of the Managed Data Service team, working with our customers to support their data-ops and data projects • be involved in designing, building, maintaining and troubleshooting data pipelines, models and transformations • help to build, maintain and troubleshoot data observability solutions to improve data quality • help turn data into valuable business insights, preparing custom solutions optimised for each client

• Design, develop, and maintain scalable data pipelines to ingest, transform, validate, and operationalize data from a variety of enterprise sources • Build and support data integration workflows across structured and unstructured data sources, including APIs, files, cloud storage, databases, and enterprise platforms • Enable analytics and reporting use cases by preparing curated datasets and supporting integration with BI and analytics tools such as Tableau, Databricks, Palantir, Power BI, Qlik, or similar platforms • Support the development of data models, transformation logic, and reusable engineering patterns that improve data access, decision support, and operational visibility • Collaborate with analysts, engineers, and stakeholders to refine requirements and deliver data products aligned to mission needs • Support testing, debugging, validation, and performance tuning of data pipelines and analytics workflows • Leverage Git-based repositories and version control best practices to manage engineering efforts • Participate in Agile development activities, including sprint planning, backlog refinement, demos, and retrospectives • Document technical designs, data flows, transformation logic, and engineering processes to support maintainability and knowledge transfer • Contribute to secure, scalable, and repeatable delivery practices across cloud and DevSecOps environments.

• You will work closely with finance, accounting, billing, and revenue teams to understand their main pain points and translate them into self-serve, reliable, and scalable data solutions • You are expected to mentor other team members and be a champion of engineering excellence across the organization • You will be part of a small team with a large amount of ownership and autonomy, managing initiatives directly from design through execution • You will have the freedom to suggest and drive organization-wide initiatives that shape the financial data vision and roadmap • You will own critical data integration pipelines and models, ensuring uniform, reliable, timely, and accurate financial reporting

Role Description As an ML Engineer, you will join a project to build a face search pipeline for an iOS app that helps people find where their photos appear online. You will play a role in building a leading cybersecurity product that protects users' photos online. The engagement is fully remote, with a weekly demo to the core team. What you will build: - A web crawler that continuously indexes publicly available photos from the open web. - A face inference layer – detection, alignment, embedding – using a model of your choice. - A vector index for fast face-match search across hundreds of millions of embeddings. - A search API that returns matches by uploaded photo, with source URLs. - A re-crawl and monitoring loop that alerts users when new photos of them appear online. You choose the architecture – which face model, which vector database, which crawler framework – and own those choices. Qualifications - Strong experience with Python and modern ML inference stacks (PyTorch, ONNX, TensorRT, or similar). - Strong AI proficiency: you keep up with modern techniques and tools, and you work fast with AI-assisted workflows (LLM coding/debugging, rapid prototyping) while still writing production-quality systems. - Proven experience shipping a face recognition or image similarity system in production. - Experience operating a non-trivial web crawler – you know the difference between one million and one hundred million URLs. - Experience with vector databases and approximate nearest neighbour search (FAISS, Milvus, Qdrant, pgvector, etc.). - Awareness of the legal landscape around open-web face indexing in 2026 (GDPR, BIPA, EU AI Act, takedown obligations). - Comfort working asynchronously and achieving results. Requirements - Experience with GPU serving infrastructure (Triton, BentoML, Ray Serve, Modal, RunPod, etc.). - Experience with distributed crawling frameworks (Scrapy, Playwright, Crawlee) and proxy/residential rotation. - Experience with cost optimisation for GPU and storage at scale. - A portfolio link, a public write-up, or an open-source project we can look at. Benefits - The ability to contribute to making the world’s best technology for protecting people’s photos from deepfakes and identity theft online. - A clearly scoped project with direct access to the founders. - Full ownership of the technical decisions in this domain for the duration of the contract. - Compensation was discussed directly with you. - A clean handoff at the end, with the option to extend the engagement if it works for both sides. We look forward to receiving your CV and learning more about your experience! Dear Candidates, due to a high volume of applications, only selected candidates will be contacted for interviews. We appreciate your understanding. Thank you for considering a career with us.