Machine Learning Engineer

Senior Machine Learning Engineer – Applied AI / Agent Systems

Company : AdsGency AI

Relocation to San Francisco City Required

We’re

AdsGency AI

— 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.

Location :

Onsite (San Francisco City)

Employment Type : Full-Time

Relocation to San Francisco City Required

We Sponsor OPT / CPT / STEM-OPT / DO NOT sponsor H1B Transfer

About AdsGency AI

We’re

AdsGency AI

— 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.

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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