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Software Reliability Engineer Jobs

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Explore current software reliability engineer jobs. Use filters to narrow by work mode, employment type, experience and date posted.

F
Forto
πŸ“ Berlinβ€’ Full-time
1mo ago

About Us What if your work could drive change in a globally established industry, shaping processes that touch every corner of the world? At Forto, we are at the forefront of change, harnessing the power of AI to revolutionise logistics. We want to reinvent digital supply chains to be transparent, frictionless and sustainable. From day one, our mission has been to simplify global trade – creating a seamless and efficient logistics process. Your role & Mission The mission of the Flash team at Forto is to fundamentally redesign operations processes by leveraging automation and intelligent decision-making to handle shipments more efficiently. We aim to scale CoPilot so that it becomes the primary system used by operations managers, enabling them to be more effective and efficient in their daily work. As a Senior Software Engineer in the Flash team, you will help build AI-driven solutions and the CoPilot that powers our logistics operations. You will maintain and evolve a sophisticated event-driven, distributed architecture designed to answer one key question: How do we improve shipment handling and bring efficiency to our operations teams at scale? From quotation and rate management, to shipment execution and schedule optimization (capacity utilization and GP optimization), to carrier integrations and automated data extraction from unstructured sources (emails, PDFs, spreadsheets), this role focuses on building reliable, data-heavy systems that directly impact revenue and operational performance. What you will do Design, build, and evolve scalable and resilient backend systems Contribute to an event-driven, distributed architecture Work on AI-adjacent systems (integration, orchestration, data-heavy workflows) Own services end-to-end (design, implementation, documentation, operation) Collaborate closely with product managers, operations, and other engineers Communicate clearly with stakeholders and explain technical trade-offs Contribute to technical standards and b

typescriptnode.jsmongodb
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Plaid
πŸ“ San Franciscoβ€’ Full-time
1mo ago

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Plaid Protect is a real-time fraud intelligence product built on a unique advantage: Plaid’s network-level visibility across bank accounts, devices, identities, sessions, institutions, applications, and financial behavior. Protect helps customers detect first-party fraud, synthetic identities, account takeovers, and coordinated attacks that are difficult to see from a single application, account, or transaction. Trust Index turns that fraud intelligence into real-time fraud scores and actionable attributes. This team builds the systems that make this intelligence possible: low-latency inference, new data and model integrations, customer-facing APIs and attributes, safe rollouts, and feedback loops. Ti3 expanded Plaid’s fraud graph nearly 10x and, in early testing, detected up to 41% more fraud at the same false-positive rate. Learn more about Ti2 and Ti3 . We are a small, high-agency team working closely with Product, Data Science, and Machine Learning. We value demos over docs, conviction over consensus/alignment, builder schedule over meeting-heavy calendars. We’re scrappy and a talent-dense team that has high agency and high ownership. As a Staff Software Engineer on the Protect Core team, you wi

awsrestmachine learning
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P
Plaid
πŸ“ New Yorkβ€’ Full-time
1mo ago

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. We believe Plaid has the power to be the next-gen Credit Bureau - supporting large scale adoption of cash flow into the credit underwriting process. The Credit Decisioning platform team is responsible for building best-in-class cashflow based insights products that enable lenders to make more holistic lending decisions and empower broader access to Credit products for prospective borrowers. We own the systems and tooling that form the platform to build and serve these insights at huge scale, partnering with our Data partners to release new products yearly. You will be defining the future architecture of Credit insights products and executing against an ambitious product roadmap. You will partner with our Product, Data Science, and Machine Learning team to iterate on and productionize new insights that enable our customers to make more holistic lending decisions. Responsibilities: Leading technical architecture and execution across credit insights products: everything from data fetching and online feature serving for API requests, to offline production pipelines and tooling for model training. Scaling and evolving the architecture through an expected ~100x increase in load from deterministic factors

awsmachine learningai
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