Senior ML Engineer

Text Us Services, Inc.; Denver (CO); On-site; USD 180,000 - 200,000; Full time

Benefits offered by this job

HSA contributions

401(k) with company match

Unlimited PTO

Cell phone and internet reimbursement

One-time $1,000 home office stipend

Job summary

Text Us Services, Inc. is seeking an experienced Machine Learning Engineer to enhance its AI-native engineering organization. This full-time role focuses on designing and implementing a robust ML Ops platform, enabling engineers to effectively engage with AI for product development.

Candidates should have at least 6 years of engineering experience, strong applied LLM expertise, and proficiency in Python. The position offers a competitive salary range of $180‑200K and a hybrid working model based in Denver, CO.

Qualifications

• Experience in building and maintaining ML models.
• Knowledge of evaluation, RAG, and prompt engineering.
• Strong foundation in cloud-native infrastructure.

Responsibilities

• Design and implement ML Ops platform for engineers.
• Monitor model performance and ensure stability.
• Collaborate to integrate AI features across the product.

Skills

ML platform experience

Strong applied LLM experience

Proficiency in Python

Communication skills

Education

6+ years engineering experience

Tools

AWS

Ruby on Rails

Containers

Job description

TextUs is on a mission to revolutionize business communication by enabling seamless and impactful engagement between workers and consumers. Our focus is on innovation, ease of use, and delivering measurable results through tools that outpace other messaging solutions and foster trust for customers and stakeholders.

Every team member is empowered to make a difference. A collaborative, data‑driven culture ensures you have the resources and support to excel in building the future of mobile‑first, conversational engagement.

Responsibilities

Our product is evolving from a feature‑centric AI to a layer that permeates the entire stack: response suggestions, abuse detection, summarization, lead scoring, and intent classification. Building the engineering layer that supports rigorous ML systems is essential.

Key tasks include designing an ML Ops platform that allows any engineer in the organization to use model registries, feature pipelines, and deployment pathways.

• Model registry, feature pipelines, and deployment pathways that any engineer in the org can use
• Evaluation infrastructure that catches regressions before they hit production
• Drift detection, online evaluations, cost and latency monitoring
• Rollback and progressive rollout patterns tailored for ML systems

Applied AI in the Product

• LLM‑powered features built on frontier APIs: prompt engineering, retrieval, structured generation
• Evaluation frameworks that assess real effectiveness
• Cost and latency budgets to stay within constraints
• Human‑in‑the‑loop feedback loops that improve features over time

Models We Own

• Small specialized classifiers: intent, opt‑out, urgency, abuse
• Selective fine‑tuning when data, task, and economics align
• Inference infrastructure capable of campaign‑volume load

Judgment and Patterns

• Build‑vs‑buy decisions: when to use frontier APIs, managed services, fine‑tuning, or regex
• Guardrails so product engineers ship AI features without becoming ML experts
• Clear and defensible viewpoints on customer data use and handling

How AI Fits

We are an AI‑native engineering org. AI is expected to be used heavily in all work, driving changes in ML production through synthetic evaluation, automated regression detection, and faster experimentation.

Engineers will also be the go‑to experts for adding AI features across the product roadmap.

Who You Are

• 6+ years of engineering experience, including at least 3 years focused on an ML platform, ML Ops, or applied ML in production
• Experience with building and maintaining ML models, ensuring they break less often
• Strong applied LLM experience with knowledge of evaluation, RAG, prompt engineering, and production considerations
• Proficiency in Python across the modern ML stack; familiarity with Ruby on Rails for product integration
• Depth in cloud‑native infrastructure (AWS preferred); containers, IaC, production operations
• Track record of sound build‑vs‑buy decisions
• Excellent communication skills—able to explain model behavior to PMs and backend engineers

Bonus

• Real fine‑tuning experience on open models through end‑to‑end production
• Experience with conversational AI, NLP, or messaging products
• Knowledge of PII handling and data governance for ML systems
• Background in a smaller engineering org where multiple hats were worn

How We Work

• Small teams with real ownership; build the ML stack with a long‑term vision
• AI‑native by default; you are expected to use AI and related tools as part of daily work
• Outcome‑driven culture—focus on safe, high‑quality delivery rather than busy dashboards
• Hiring for judgment; tooling evolves but core instincts for breaking points remain stable
• Initial Call with HR (30 min via Video)
  • Topics: Culture, logistics
• Interview with Hiring Manager (45 min via Zoom Video)
  • Topics: Culture, skills, role overview
• Take‑Home Assignment
  • ML & AI Focus Exercise
• Interview with Cross‑Functional Team (60 min via Zoom Video)
  • Topics: Culture, leadership, skills, role overview
• Prepared questions about the role, team, and product to ensure fit

Employment Details

• Job Type: Full time
• Compensation Range: $180‑200K
• Location: Hybrid—Headquartered in Denver, CO
• Target Start Date: 2 weeks from offer date
• # hires for this role: 1
• Reporting to: Doug Busley, SVP Engineering

Benefits

• Competitive pay
• HSA contributions
• 401(k) with company match
• Unlimited PTO
• Cell phone and internet reimbursement ($100/month)
• One‑time $1,000 home office stipend after 6 months with TextUs
• U.S. remote first with optional WeWork office space in downtown Denver, CO

TextUs does not discriminate based on race, color, religion (creed), gender, gender expression, age, national origin (ancestry), disability, marital status, sexual orientation, or military status. We are committed to providing an inclusive and welcoming environment for all staff, volunteers, subcontractors, vendors, and clients.