Machine Learning Engineer

About AdsGency AI

AdsGency AI is an AI-native startup building a multi-agent automation layer for digital advertising. Our system uses LLM and ML-driven agents to autonomously launch, scale, and optimize ad campaigns across Google, Meta, TikTok, and more — no human marketer required.

Our mission : build the operating system where AI runs performance marketing better than humans ever could.

We’re backed by top-tier investors and moving fast. This is your chance to join early — and help design the ML foundation that powers the next evolution of ad intelligence.

Relocation to San Francisco City required. Onsite location. Employment type : Full-Time. We sponsor OPT / CPT / STEM-OPT, no H1B Transfer.

The Role – Senior Machine Learning Engineer

As a Senior Machine Learning Engineer, you’ll design, train, and deploy AI models that drive AdsGency’s agent intelligence — from ad performance prediction to cross-channel optimization and creative generation.

You’ll bridge the gap between data science, engineering, and systems design, shaping the brain of our multi-agent OS.

This role sits at the core of AdsGency’s intelligence layer — where models don’t just predict, but act.

What You’ll Build

Agent Intelligence Models : Develop and fine-tune models that predict campaign performance, bid pacing, and creative success.

Reinforcement & Decision Systems : Build RL and multi-objective optimization frameworks enabling agents to learn from feedback and improve autonomously.

LLM + ML Hybrid Systems : Integrate generative agents (OpenAI, Claude, LangGraph) with quantitative models for adaptive decision-making.

Data Pipelines : Architect and maintain scalable feature pipelines and embeddings for multi-platform ad data.

Measurement & Attribution : Design models to unify performance signals across Google, Meta, TikTok, etc., handling delayed and biased feedback.

Experimentation Frameworks : Develop A / B testing and counterfactual learning systems to validate model improvements.

ML Infrastructure : Own the training → evaluation → deployment lifecycle using modern MLOps practices (e.g., Weights & Biases, Airflow, Docker).

Tech Stack

Modeling & ML : PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, Hugging Face, Transformers

Languages : Python, Go (for systems), SQL

Infra & MLOps : AWS / GCP, Docker, Kubernetes, Airflow, Weights & Biases, MLflow

Data Systems : Kafka, PostgreSQL, Redis, Supabase, Qdrant / Weaviate (vector DBs)

AI Layer : OpenAI, Claude, LangChain, LangGraph, CrewAI

What You Bring

4–8 years of experience in ML engineering or applied data science.

Strong foundation in ML algorithms, model lifecycle, and feature engineering.

Proficiency in Python and ML frameworks (PyTorch / TensorFlow).

Experience building models that go into production, not just notebooks.

Understanding of distributed systems, data pipelines, and model serving.

Experience with A / B testing, reinforcement learning, or online learning.

Curiosity about how LLMs and agents can augment traditional ML systems.

Startup mindset — fast iteration, ownership, and bias for impact.

Bonus Points

Experience in AdTech / MarTech, especially prediction, attribution, or bidding systems.

Experience integrating LLMs with structured data pipelines.

Knowledge of reinforcement learning, causal inference, or bandit algorithms.

Prior work in early-stage or high-growth startups.

Strong sense of product impact — you ship models that move metrics.

Why Join AdsGency AI?

Competitive salary + meaningful equity.

Core ownership in a fast-scaling AI company.

Work directly with founders and research engineers on frontier agentic systems.

Culture of speed, autonomy, and craftsmanship — no corporate bureaucracy.

Build systems that redefine how advertising learns and optimizes itself.

Visa sponsorship (OPT / CPT / STEM-OPT / no H1B Transfer).

Industry : AI & Software Development. Employment type : Full-Time. Location : Onsite (San Francisco City).

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