TextUs
Technology
SeniorMLEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior ML Engineer at TextUs. Skills: ML platform, ML Ops, Applied ML, LLM. Own ML and AI engineering layer. Build ML Ops platform”
What You'll Achieve.
Ship ML systems with rigor; Catch regressions before prod; Stay inside cost budgets; Stay inside latency budgets; Make features measurably better
Industry & Context.
Root cause analysis; Troubleshooting; Build-vs-buy decisions
On call for models
What They're Looking For.
Must Have
6+ years engineering experience, 3+ years ML platform experience, 3+ years ML Ops experience, 3+ years applied ML production experience, On-call for models experience, Applied LLM experience, Comfortable in Python, Comfortable in Ruby on Rails, Cloud-native infrastructure depth, Build-vs-buy decisions track record, Clear communicator
Nice to Have
Real fine-tuning experience, Conversational AI experience, NLP experience, Messaging products experience, PII handling experience, Data governance for ML experience, Background in smaller engineering org
What You'll Do.
Own ML and AI engineering layer
Build ML Ops platform
Build deployment pathways
Build evaluation infrastructure
Build drift detection
Build cost monitoring
Build latency monitoring
Build rollback patterns
Build progressive rollout patterns
Build LLM-powered features
Build structured generation
Build eval frameworks
Manage latency budgets
Build human-in-the-loop feedback
Build specialized classifiers
Build inference infrastructure
Make build-vs-buy calls
Build guardrails for engineers
Define data usage policies
Generate synthetic eval
Detect regressions automatically
Speed up experimentation
Help engineers add AI features
How You'll Work.
Team & Collaboration
Cross-functional teams; Product engineers
Communication Scope
Explain model behavior; Explain inference pipeline
Full Job Description
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: pro
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