Senior AI Engineer, Marketing Systems

Curate Partners; Boston (MA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Curate Partners is seeking an AI/ML engineer to design, build, and deploy production-grade, agentic workflows that support marketing objectives. You will integrate agents with Salesforce and the broader martech stack, ensuring data models and API layers are clean and scalable.

You will combine licensed data, public sources, and internal first-party data into reliable pipelines, while collaborating with marketing to translate vague goals into concrete automation.

Qualifications

• Experience building AI and agentic capabilities inside a marketing organization.
• Shipped agents or AI automation for marketing, demand generation, or revenue teams.
• Understanding funnel mechanics, lead qualification and routing, ICP and account scoring.
• Salesforce fluency at the practitioner level (data model, API integration).
• Strong software engineering fundamentals in Python and/or TypeScript.
• Experience integrating third‑party and licensed data providers, including entity resolution and enrichment quality.
• Comfort working embedded with a client team with light supervision.

Responsibilities

• Design, build, and deploy production agentic workflows against marketing objectives.
• Integrate agents with Salesforce and the martech stack respecting data models.
• Combine licensed data providers, public sources, and internal data into agent pipelines.
• Collaborate with marketing to translate business goals into automated solutions.
• Build evaluation and guardrail layers for accuracy, reviews, cost, and latency controls.
• Document patterns and train internal teams to sustain the capability post-engagement.

Skills

AI engineering

Salesforce fluency

Python

TypeScript

API integration

Data enrichment

Tools

Salesforce

Claude

Marketo

HubSpot

Pardot

Job description

• Design, build, and deploy production agentic workflows against real marketing objectives, including research, enrichment, segmentation, content operations, and campaign and lifecycle automation
• Integrate agents with Salesforce and the surrounding martech stack, respecting existing data models, object structures, and operational hygiene
• Combine licensed data providers, public web sources, and internal first‑party data into reliable, evaluated agent pipelines
• Work alongside marketing stakeholders to translate ambiguous business goals into scoped, shippable automation
• Build the evaluation and guardrail layer, including accuracy checks, human review points, and cost and latency controls, so marketers can trust the output
• Document patterns and train internal teams so the capability sustains after the engagement ends

Required Experience

• Demonstrated experience building AI and agentic capabilities inside a marketing organization. You have shipped agents or AI automation for a marketing, demand generation, or revenue team, and you already understand funnel mechanics, lead qualification and routing, ICP and account scoring, attribution, list hygiene, and how marketing and sales hand work back and forth.
• Hands‑on production work with Claude, including agentic patterns such as tool use, multi‑step orchestration, structured outputs, retrieval, prompt and context engineering, and agentic coding workflows
• Salesforce fluency at the practitioner level, covering the data model, API integration, and how marketing data lives and moves inside a CRM
• Strong software engineering fundamentals in Python and/or TypeScript, API integration, and the ability to stand up and maintain services independently
• Experience integrating third‑party and licensed data providers, including entity resolution, deduplication, and enrichment quality
• Comfort working embedded with a client team, operating with light supervision, and driving from a vague problem statement to a working system

Strongly Preferred

• Healthcare, health tech, provider, or payer domain exposure. Value‑based care familiarity is a significant advantage.
• B2B marketing to provider organizations, health systems, or physician practices
• Experience with the marketing automation layer adjacent to Salesforce, such as Marketo, HubSpot, or Pardot
• Track record as the first AI engineer embedded in a non‑technical function

What Success Looks Like

• 30 days: Grounded in the current manual research workflow, data sources, and Salesforce structure, with a first working agent prototype in front of stakeholders
• 60 days: Research agent running against real prospect volume, with measured accuracy and clear time savings against the manual baseline
• 90 days: Output trusted and adopted by the marketing team, a second use case scoped and underway, and patterns documented for internal reuse