Senior Applied Machine Learning Engineer

Respondology

Senior Applied Machine Learning Engineer developing LLM-powered systems for an AI-driven social engagement platform. Working collaboratively across teams to improve and implement ML features in production environments.

Posted 7/27/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $140,000 - $175,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying applied ML systems, with a strong focus on LLMs and evaluation methodologies. Capable of developing internal tooling and reference implementations to enhance engineering practices in AI.

Highest-signal resume keywords

Applied ML Systems DevelopmentLLM ExperienceML Evaluation TechniquesPython ProgrammingInternal Tooling Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Applied MLLLMsClassificationSummarizationRAGAgentic WorkflowsDataset ConstructionOffline EvaluationOnline EvaluationMetric Definition

Soft Skills

OwnershipCollaborationProblem-Solving

Tools & Technologies

Eval PipelinesObservability ToolingReference Implementations

Industry Keywords

AI ProductsGreenfield DevelopmentEngineering Collaboration

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Partnering closely with our AI Function lead, you will work across all three of our AI products: Moderate, Discover, and Respond
Your focus will be on the highest-risk applied ML work and strengthening the foundations (eval harnesses, observability, reference implementations) that make AI work repeatable for engineers who aren't ML specialists
Own high-difficulty applied ML features end-to-end, from prototype through production
Build eval pipelines, golden datasets, and observability tooling (much of this is greenfield)
Create reference implementations that show other engineers how to ship AI the right way
Partner with Product and Engineering to translate between what's technically feasible and what teams are trying to build

Requirements

What you’ll need

5+ years building applied ML systems in production
Hands-on experience with LLMs, including classification, summarization, RAG, and agentic workflows
Strong ML evaluation instincts: constructing datasets, running offline and online evals, defining metrics
Ability to own a feature end-to-end without a lot of hand-holding
Strong Python skills and comfort working in a production codebase
Experience building internal tooling or platforms that leveled up other engineers is a big plus

Benefits

Comp & perks

Competitive salary + equity
100% remote, with optional access to our Boulder, CO office
Flex PTO, generous holidays, and a full week off between Christmas and New Year's
Multiple healthcare options including FSA/HSA plans
401k (traditional and Roth) with company match, immediately vested
Fully paid family and parental leave (12 weeks per year)
Twice-yearly team offsites