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A fast-growing, remote-first tech company seeks a Machine Learning Engineer to embed intelligence into their data activation products. You'll tackle personalization, predictive modeling, and content generation challenges, driving data-informed decision-making. This senior-level role offers significant impact and growth potential in a supportive culture.
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This range is provided by Willing Tech. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
$200,000.00/yr - $260,000.00/yr
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Machine Learning Engineer, AI Decisioning
Remote – North America
Coming from top 50 CS or EE schools and degrees.
This fast-growing, remote-first tech company is on a mission to empower teams across industries to take action on their data. With a customer base that includes global names in e-commerce, SaaS, and retail, their platform enables organisations to collect, prepare, and activate customer data from their data warehouses to power personalised experiences and business operations.
The company recently introduced an AI Decisioning platform that helps marketers move beyond manual processes by leveraging AI to drive 1:1 personalisation. The platform allows business users—not just technical teams—to deliver impactful, data-driven customer interactions by setting goals and guardrails for AI agents.
With a supportive, impact-driven culture, the team is known for its fast execution, thoughtful collaboration, and strong values. The company has global team members, a strong funding history including a recent Series C that brought them to unicorn status, and backing from top-tier investors in the tech ecosystem.
As a Machine Learning Engineer, you'll be part of a new initiative focused on embedding intelligence directly into the platform’s data activation products. The team is building a foundational intelligence layer designed to guide companies on who to target, what content to serve, and when to engage, transforming guesswork into data-informed decision-making.
Key problem areas include:
As one of the early ML engineers, you’ll work across the stack—engaging in problem definition, modeling, infrastructure, and customer collaboration. This is a senior-level role with a focus on impact and growth potential, not just years of experience.
Ideal Background:
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