AI/ML Engineer
Role Overview
This senior-level AI/ML Engineer role involves designing, building, and deploying large-scale NLP and LLM systems for classification, ranking, clustering, topic discovery, and content generation. The engineer will own workflows from data to deployment, collaborate with product and engineering teams, and transform user conversations into production features and publish-ready content to drive visibility and engagement. The role requires shipping ML systems in production, working with cross-functional teams, and having a significant impact on customer-facing KPIs in a fast-paced startup environment.
Perks & Benefits
The job is remote, offering flexibility in location, though it may involve collaboration with an NYC-based team, possibly requiring alignment with Eastern Time hours. Benefits include competitive compensation, meaningful equity, a fast-paced environment with trust and autonomy, and opportunities for career growth through ownership of the product analytics function and close collaboration with product, engineering, and go-to-market teams. The culture emphasizes speed, iteration, and impact, suitable for builders in a high-performance setting.
Full Job Description
Profound is on a mission to help companies understand and control their AI presence. As an AI/ML Engineer, you will design, build, and ship large scale NLP and LLM systems that power classification, ranking, clustering, topic discovery, and content generation. You will own workflows from data to deployment, partner across product and engineering, and turn real user conversations into production features and publish-ready content that drives visibility, engagement, and conversion.
What You’ll Do
Build and deploy NLP models at scale for classification, ranking, clustering, topic extraction, and summarization
Design LLM workflows for context and content generation end-to-end: topic discovery → brief creation → outlines/drafts → revision loops → publish-ready assets
Develop prompt and template libraries aligned to brand voice and channel (blog, landing pages, help docs, ads), with retrieval for evidence-grounded generation and citations
Create eval frameworks for generated content (factuality, coverage, tone, safety, originality) with rubric-based LLM evals, human-in-the-loop review, and red-teaming
Instrument content performance (AEO/SEO visibility, engagement, conversion) and run experiments to improve quality, cost, and latency
Transform large text datasets into production features and signals that drive product insights
Partner with engineering to instrument events, maintain data pipelines, and uphold high data quality and observability
Collaborate with product, data, and go-to-market on success metrics and experiments that move customer-facing KPIs
What We Expect from You
Proven experience shipping ML systems in production at scale, especially with large text data
Hands-on experience building LLM content systems (prompting, templating, retrieval/RAG, guardrails, evals)
Fluency in SQL and strong Python skills with modern ML tooling
Strong grasp of ML and generation quality metrics; ability to design offline/online evals and monitoring
Ability to innovate when off-the-shelf doesn’t fit the problem
Experience working in cross-functional, high-performance teams
Clear communication with technical and non-technical partners
Ownership mindset and comfort operating in a fast-paced environment
What You’ll Get Out of It
Ownership of the entire product analytics function at an early stage company
A key role in shaping how we measure usage and make product decisions
Close collaboration with product, engineering, and go to market teams
A fast paced environment with a lot of trust and autonomy
Competitive compensation and meaningful equity
This is an on-site role in our Union Square office—designed for builders who thrive on speed, iteration, and impact.
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