Senior ML Engineer

TextUs•Denver, CO

9h•$180,000 - $200,000•Hybrid

About The Position

TextUs is seeking a Senior ML Engineer to revolutionize business communication by enabling seamless and impactful engagement between workers and consumers. This role is crucial in shifting our product from having AI as a feature to AI as a foundational layer across all aspects, including response suggestions, abuse detection, summarization, lead scoring, and intent classification. The ideal candidate will treat ML systems with the same rigor as other production systems, making pragmatic AI choices, leveraging frontier model APIs with retrieval and prompt engineering, and selectively training or fine-tuning models when justified. This position owns the end-to-end ML and AI engineering layer, including the ML Ops platform and applied AI features within the product.

Requirements

6+ years of engineering experience
At least 3 years focused on ML platform, ML Ops, or applied ML in production
Experience being on-call for models and understanding failure modes
Strong applied LLM experience, including opinions on evaluation, RAG, and prompt engineering
Ability to differentiate between a demo and a production system
Proficiency in Python and the modern ML stack
Comfort integrating with Ruby on Rails
Depth in cloud-native infrastructure (AWS preferred), including containers and IaC
Track record of sound build-vs-buy decisions
Clear communication skills, able to explain technical concepts to both product managers and backend engineers

Nice To Haves

Real fine-tuning experience on open models, end-to-end through production
Experience with conversational AI, NLP, or messaging products
Familiarity with PII handling and data governance for ML systems
Background in a smaller engineering organization with broad responsibilities

Responsibilities

Own the ML and AI engineering layer end to end.
Build and maintain the ML Ops platform, including model registry, feature pipelines, deployment pathways, evaluation infrastructure, drift detection, cost and latency monitoring, and ML-specific rollback/progressive rollout patterns.
Develop and implement LLM-powered features using frontier APIs, focusing on prompt engineering, retrieval, and structured generation.
Create evaluation frameworks to assess the effectiveness of AI features.
Manage cost and latency budgets for AI features and perform engineering work to stay within them.
Implement human-in-the-loop feedback loops to continuously improve features.
Develop and deploy small, specialized classifiers (e.g., intent, opt-out, urgency, abuse) where appropriate.
Perform selective fine-tuning of models when the task, data, and economics align.
Ensure inference infrastructure can handle campaign-volume load.
Make informed build-vs-buy decisions, determining the best approach for ML solutions.
Establish guardrails for product engineers to ship AI features without requiring deep ML expertise.
Define and enforce policies on customer data usage and handling for ML systems.
Leverage AI tools like Claude Code heavily for personal work and to drive AI adoption across the engineering organization.
Serve as a go-to resource for other engineers seeking to integrate AI features, ensuring they gain knowledge from interactions.