Machine Learning Engineer

Trend Capital Holdings, Inc.•Vancouver, WA

17d

About The Position

We are seeking an experienced and innovative Machine Learning Engineer with a background in digital marketing and campaign optimization. You will design, test, and deploy AI-driven systems that monitor and predict ad performance, translating ML insights into revenue impact. This position requires someone who thinks in both technical and business terms. You will develop ML that will automate data collection to draw meaningful insights to help automate ad campaigns and optimizations. You will take projects from concept to production, constantly looking for ways to create self-running, efficient processes using the latest AI tools. This position is ideal for someone passionate about cutting-edge ML applications, business impact, and turning complex data into automated, revenue-driving solutions.

Requirements

2+ years of experience in machine learning / AI development - either developing proprietary models or effectively deploying and customizing open source models.
Strong experience with integrating AI tools, and the ability to build production-grade applications using them.
Demonstrated “tinkerer/hacker” mindset.
Comfort with experimentation, rapid prototyping, and learning new tools quickly.
Coding fundamentals.

Nice To Haves

Master’s degree (or equivalent experience) in Artificial Intelligence, Data Science, Statistics, or related field.
Experience in data science / business intelligence.
Proven track record designing and deploying automated decision systems, ideally in digital advertising or campaign optimization.
Strong understanding of marketing and financial metrics: ROAS, CAC, LTV, attribution, incrementally, and statistical significance.
Hands-on experience working with marketing APIs (Meta, Google Ads, TikTok, Snapchat, etc).
Software engineering experience.

Responsibilities

Design, build, and maintain ML-driven systems and automations that improve marketing performance, budgeting, attribution, or creative optimization.
Utilize AI tools and AI APIs to rapidly prototype and deploy solutions that enhance internal workflows and consumer-facing products.
Integrate with marketing platform APIs (Meta, Google Ads, TikTok, etc.) to collect data, automate decisions, and optimize campaigns.
Build automated models and decisioning pipelines for areas such as bidding, targeting, media mix modeling, or multi-touch attribution.
Use statistical rigor to evaluate experiments, model performance, and marketing outcomes.
Collaborate with engineering, product, and marketing teams to translate business needs into ML-powered solutions.
Analyze core financial and growth metrics including ROAS, CAC, LTV, and spend efficiency and incorporate them into optimization logic.