AI ML Engineer

\*\*\* To apply, please submit the following with your resume and at least two references \*\*\*

One-page Impact Case Study (PDF)

• Problem and context (dataset, constraints).
• Your role and concrete decisions.
• Approach (LLM/RAG, prompts/templates, guardrails or evals used).
• Results with real metrics (quality, latency, cost; any visibility/engagement/conversion impact).
• What you'd change on a v2.

Code Sample (link) with tests

• Either: a small LLM workflow (prompt + minimal retrieval + inline citations + tiny eval check), or a self-contained ML module you wrote that's production-grade (tests, docstrings).
• Include a short README explaining design choices and trade-offs.

The work:

• Build, deploy, and scale NLP models for classification, ranking, clustering, topic extraction, and summarization
• Design and implement end-to-end LLM workflows: topic discovery → brief creation → outlines/drafts → revision loops → publish-ready assets
• Architect and maintain backend systems and APIs that power AI features, including task queues, retry logic, error handling, and priority routing
• Deploy and manage AI models on cloud infrastructure (AWS/GCP/Azure), owning reliability, cost optimization, and performance tuning
• Identify and resolve cloud-side bottlenecks, latency, throughput, scaling, to keep production AI workloads fast and cost-efficient
• Develop prompt and template libraries aligned to brand voice and channel (blog, landing pages, help docs, ads), with retrieval for empirically-grounded generation and citations
• Create evaluation frameworks for generated content with rubric-based LLM evals and human-in-the-loop review
• Instrument content performance (GEO/SEO visibility, engagement, conversion) and run experiments to improve quality, cost, and latency
• Transform large text datasets into production features and metrics that drive product insights
• Develop instrumented events, maintain data pipelines, and uphold high data quality
• Help define product, data, and market-ready success metrics that advance customer analytics
• Design, execute, synthesize, and publish AI search experiments

Education / Experience Requirements:

• Proven experience shipping AI systems in production at scale, especially with large text data
• Hands-on experience building LLM content systems (prompting, templating, retrieval/RAG, guardrails, evaluations)
• Solid backend engineering skills, API design, task queues (e.g. Celery/Redis), database architecture, and service reliability
• Experience deploying and operating ML/AI models on cloud platforms (AWS, GCP, DO), including infrastructure management and performance optimization
• Fluency in SQL and strong Python (Django) skills with modern tooling
• Strong understanding of ML and generation quality metrics; ability to design offline/online evals and monitoring
• Ability to innovate when standard solutions don't solve the problem
• Experience working in cross-functional, high-performance teams
• Clear communication with technical and non-technical partners
• Comfortable operating in a fast-paced environment with tight sprint release dates

We're looking for a person that:

• Pays attention to the subtle details that make a great user experience
• Is comfortable working on multiple projects with simultaneous deadlines
• Demonstrates the ability to research and solve problems end-to-end, from model logic to infrastructure
• Keeps up with the latest tools and has a firm understanding of when to use them
• Has advanced knowledge in building and integrating full-stack AI systems, backend services, cloud deployments, and intelligent application layers
• Understands and empathizes that there's a person on the other side of the screen using what we build
• Has experience integrating third-party APIs and managing their reliability in production
• Can effectively and thoroughly QA and document their work