Machine Learning Engineer, Decision Systems (Ad-Tech AI)
ScrumAID
San Francisco, CA · Posted November 1, 2026
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Among computer occupations, all other, AI is already used for a large share of the tasks. Measured exposure for computer occupations, all other: 31% (high).
Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.
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About the job
An early-stage company is building an autonomous platform that runs digital advertising campaigns, with real budgets at risk. This engineer would own the decision layer that determines what happens to every campaign, while a separate platform engineer handles supporting tools.
Responsibilities
Develop recommendation and scoring logic for bid adjustments, budget moves, pausing or boosting, and conversion-postback tuning
Set out the way the platform learns from results so strategy keeps improving rather than restarting
Write optimization policies for noisy live data, ranging from basic rules to post-training and reinforcement learning
Plan how several agents reason about a campaign’s situation and cooperate, and assess what they output
Be answerable for the quality of decisions and the return on ad spend rather than for infrastructure
Qualifications
Three or more years shipping production ML or optimization systems
A closed-loop decision system you have delivered, where logged outcomes change later decisions
Reinforcement learning, bandits or policy optimization applied to a live system
A quantitative degree such as engineering, physics, math, computer science or statistics
Statistical reasoning on noisy production data, including experiment design
Self-directed working style
Knowledge of ad optimization mechanics (pacing, spend caps, ROAS or CPA targets) is a plus
On-site in the San Francisco Bay Area. Existing visa holders can transfer; new sponsorship is not offered.