Role Description Du baust .NET-Anwendungen, die im Enterprise-Umfeld laufen. Dein Schwerpunkt liegt auf dem Backend, aber du verstehst das ganze Bild. Du übersetzt Kundenanforderungen in Architektur, lässt AI Agents Code generieren, prüfst kritisch und lieferst Qualität. Nicht Perfektion auf dem Papier, sondern ein Produkt, das funktioniert. - Du entwickelst Backend-Lösungen mit .NET und integrierst sie in AWS-Cloud-Architekturen. - Du nutzt AI CLI-Tools (Claude Code, Cursor, Gemini CLI) als Multiplikator für Entwicklung und Testing. - Du entwirfst APIs und sorgst für saubere Integration zwischen Frontend, Backend und Cloud. - Du arbeitest direkt mit Kunden an technischen Anforderungen und Lösungsdesign. - Du setzt auf automatisiertes Testing und CI/CD. - Du baust Prototypen, indem du Ideen an AI Agents gibst und den Output iterierst. Qualifications - Mindestens 3 Jahre Full-Stack-Entwicklung mit .NET-Schwerpunkt. - Erfahrung mit mindestens einem AI CLI-Tool (Claude Code, Cursor, Gemini CLI). - Erfahrung mit Cloud-Technologien (AWS von Vorteil). - Vertraut mit Git, Docker und CI/CD. - Fähigkeit, AI-Output kritisch zu bewerten und Enterprise-Qualität zu liefern. - Fließend Deutsch und Englisch. Benefits - Flexible Arbeitszeiten und 100% Remote Arbeit. - Zugang zu AI-Tools und LLM-Keys. Nicht als Benefit, sondern als Arbeitsmittel. - Echte Enterprise-Projekte mit Ownership. Produkte für Siemens, Deutsche Börse, Volkswagen. - AWS-Zertifizierungen und Konferenzen. - Ein Team von ~30 Engineers, das Wissen teilt statt hortet. - Keine Folien-Kultur. Wir arbeiten an der Sache, nicht am Konzept.

Software Engineer, Scala Palo Alto, CA Who You Are Quantifind is seeking a Software Engineer for our Platform team to help define and deliver data services and machine learning infrastructure. The Platform team owns all the infrastructure that powers the SaaS products we build for our customers. We process terabytes of data each day on our own dedicated Hadoop clusters. We use real-time data services to enable interactive investigations in our web applications. We use Kubernetes and Docker for our container ecosystem, and we develop standalone services in Scala and Python and use Spark for MapReduce jobs. You care deeply about your work and its impact on the work your team does. You hold yourself to a high standard when it comes to writing clean, performant, and maintainable code that powers well-designed microservices and enhances Quantifind’s distributed data platform. You work well with cross-functional teams that include Data Scientists, DevOps Engineers, Front-end Engineers and Product Managers. You are proactive in suggesting how to improve the design, performance, and testing of the software that you build. You can effectively balance near-term priorities with forward-looking ways to build a sustainable team culture. You value the security of a company with a proven business model and enjoy the opportunities a startup offers. Who We Are Quantifind helps some of the world’s biggest banks catch money laundering and fraud. Quantifind also works with government agencies to use the same platform to uncover criminal networks and combat money laundering committed by internationally sanctioned entities. Unlike other players in this space, Quantifind delivers results as Software-as-a-Service (SaaS) with consumer-grade user experiences. Quantifind is a data science technology company whose AI platform uncovers signals of risk across disparate and unstructured text sources. In financial crimes risk management, Quantifind’s solution uniquely combines internal financial institution data with public domain data to assess risk. While incumbent solutions based on legacy technologies demand increasingly more human resources as the operations expand, Quantifind’s SaaS solution scales by offering a way to cut through the inefficiency and enhance effectiveness through Machine Learning driven solutions that resolve for both accuracy and relevance. To help you succeed, we provide a supportive environment that fosters collaboration between teams and team members, where learning and professional growth are considered a key part of your success, and of ours. We offer a flexible work environment with a family friendly work-life balance. What A Great Candidate Looks Like: - BS or MS in Computer Science - Professional software engineer for 2+ years with a focus on platform engineering - Professional experience with Scala and/or Java - Professional experience working with PostgreSQL - Understanding of JVM internals and 2+ years of relevant experience working with code that runs in a JVM preferred - Experience with Apache Spark, and/or general MapReduce technologies preferred - You can design and prototype scalable algorithms - You can create your own efficient data structures and algorithms when the ones found in open source libraries are lacking - Professional experience writing unit tests and automated integration tests - You can build microservices with REST APIs for real-time query engines and web crawlers - You know how to set up ETL pipelines for data streams - Familiarity with storage and caching solutions and the tradeoffs among them (Memcached, Redis, etc.) preferred - Solid understanding of concurrency and distributed systems - Understanding of algorithm complexity and performance implications - Knowledge of Machine Learning techniques preferred - Great communication skills so you can work directly with our Data Scientists, DevOps Engineers, Front-end Engineers and Product Managers - Professional experience at a software startup strongly preferred - Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future The Opportunity We Offer Quantifind is seeking a Software Engineer for our Platform team in Palo Alto, California. You will work on Quantifind’s Scala infrastructure to generate risk signal results from diverse data sources such as Sanctions, PEP records, Negative News, Court & Arrest records, and much more. Quantifind’s development team is centered in Palo Alto, California, and we have technology hubs in Atlanta, Georgia, and Washington, D.C. Quantifind is currently using a hybrid mix of working from home and in the office, with regular in-person touchpoints and shared office space. A highlight of our benefits: - Competitive salary - Company Equity - Exceptional benefits package - Flexible Vacation & Paid Time Off - Employer-matched 401(k) plan - A fun environment where work-life balance is valued The base salary range for this full-time position is $130,000 to $155,000. Our salary ranges are determined by role, level, and location, and the range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location, role-related knowledge and skills, depth of experience, relevant education or training, and additional role-related considerations.

• Architect and deliver enterprise AI platform capabilities including workflow orchestration engines, multi-provider LLM integration, and automated deployment pipelines that enable healthcare teams to build and deploy AI applications at scale • Design multi-tenant infrastructure with automated provisioning, namespace isolation, and role-based access control to support secure, per-team dedicated environments across the platform • Build backend services and APIs for workflow authoring, execution orchestration, and platform observability using cloud-native patterns on Kubernetes infrastructure • Implement production observability and reliability practices including distributed tracing, performance monitoring, and incident response for platform health and cost management • Drive technical architecture decisions across the platform, mentor engineers on design patterns, security best practices, and operational excellence • Partner with business stakeholders, product teams, and engineering leadership to align platform roadmap with healthcare automation objectives and adoption strategy • Champion security posture including secrets management, audit logging, compliance requirements, and production readiness standards

• Talk to people, then build things. You'll work directly with business and engineering teams to understand what's slowing them down. You'll figure out whether the fix is traditional software, an AI integration, or some combination. Then you'll turn vague requirements into something you can actually build. • Own the whole thing. Prototype it, harden it, deploy it, monitor it. These are internal tools, so you move fast, but they still need to work reliably. • Build fast with AI-assisted development. We use tools like Claude Code to move quickly. You should be comfortable using them to scaffold, iterate, and ship. But you also need to read what they produce, catch what they get wrong, and know when to write it yourself. These tools make good engineers faster. They don't replace knowing how software works. • Use AI where it actually helps. Integrate LLM APIs (OpenAI, Anthropic, Gemini, etc.) when language understanding or probabilistic reasoning genuinely improves the outcome. Design prompt strategies and evaluation methods when you do. Skip the model call when a conditional statement would work better. Keep an eye on cost, latency, and reliability. • Write code other people can maintain. Build clean systems. Establish practical patterns for secure AI usage. Contribute to standards around observability, safety, and data handling. • Work with data. Write SQL against MySQL/ Snowflake, build internal dashboards, and turn business questions into lightweight data tools.