Job Opportunities API

WHY TEXTUS TextUs on a mission to revolutionize business communication by enabling seamless and impactful engagement between workers and consumers. With a focus on innovation, ease of use, and delivering measurable results, our strategy is rooted in creating tools that outperform other messaging solutions while fostering trust and value for our customers and stakeholders. At TextUs, every team member is empowered to make a difference. Our collaborative and data-driven culture, combined with the guidance of a proven leadership team, ensures you have the resources and support to excel. Together, we’re building the future of mobile-first, conversational engagement and redefining what’s possible for businesses and their stakeholders. RESPONSIBILITIES We're moving from a product where AI is a feature you can turn on to one where it's a layer that runs through everything: response suggestions, abuse detection, summarization, lead scoring, intent classification. That shift only works if there's an engineering layer underneath that treats ML systems with the same rigor as the rest of production. We're AI-pragmatic, not AI-maximalist. Most of what we ship will run on frontier model APIs with retrieval and good prompt engineering. Some will run on small classifiers we train ourselves. A few things will justify fine-tuning against our eleven years of conversation data. Your job is to know which is which, and to build the platform that lets us move between them without rebuilding from scratch every time. You own the ML and AI engineering layer end to end. The ML Ops platform: Model registry, feature pipelines, and deployment pathways that any engineer in the org can use Evaluation infrastructure that catches regressions before they hit prod, not after Drift detection, online evals, cost and latency monitoring Rollback and progressive rollout patterns built for ML systems, not retrofitted from generic CD Applied AI in the product: LLM-powered features built on frontier APIs: prompt engineering, retrieval, structured generation Eval frameworks that tell us whether any of it is actually working Cost and latency budgets, and the engineering work to stay inside them Human-in-the-loop feedback loops that make features measurably better over time Models we own: Small specialized classifiers where they're the right tool: intent, opt-out, urgency, abuse Selective fine-tuning when the task, the data, and the economics line up Inference infrastructure that holds under campaign-volume load Judgment and patterns: Build-vs-buy calls. Know when a frontier API is the right answer, when a managed service is fine, when to fine-tune, and when a regex would have done the job. Guardrails so product engineers can ship AI features without becoming ML experts A clear, defensible point of view on what customer data can be used for what, and how it gets handled HOW AI FITS We're an AI-native engineering org. Claude Code is at 100% licensed and roughly 80% active across engineering. You're expected to use it heavily for your own work, and to push the org on where AI changes how ML itself gets built: synthetic eval generation, automated regression detection, faster experimentation loops. You'll also be the person other engineers come to when they want to add an AI feature to something they own. The bar is that they leave the conversation knowing more than when they walked in. WHO YOU ARE 6+ years of engineering experience with at least 3 years focused on ML platform, ML Ops, or applied ML in production You've been on call for models. You know what breaks and how to design so it breaks less. Strong applied LLM experience. You have opinions on eval, RAG, prompt engineering, and where each fails. You can tell the difference between a demo and a production system. Comfortable in Python across the modern ML stack. Comfortable enough in Ruby on Rails to integrate with our