# Staff Machine Learning Engineer, Ads Measurement Modeling

**Company:** [Reddit](https://hotfix.jobs/companies/reddit)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $230k – $322k/yr
**Experience:** 7+ years
**Skills:** TensorFlow, PyTorch, Spark, Kafka, Beam, Flink, Machine Learning, Identity Resolution, Feature Engineering, Distributed Computing
**Posted:** 2026-05-04

> Leads technical strategy, architecture, and development of advanced ML models for ads identity modeling and measurement, ensuring scalability, privacy compliance, and integration across systems. Requires 7+ years software engineering with 3+ in ML at scale, expertise in ML frameworks and large-scale data processing.

## Job Description

## Minimum Qualifications

- 7+ years of professional software engineering experience, with at least 3+ years focused on ML-driven systems at scale
- Demonstrated experience architecting and building ads measurement modeling solutions leveraging advanced machine learning techniques
- Strong knowledge of various identifiers (cookies, hashed emails, phone numbers, IP addresses, user agents) and their use in identity resolution
- Proficiency in machine learning frameworks (e.g., **TensorFlow**, **PyTorch**) and libraries for feature engineering, model training, and inference
- Solid understanding of large-scale data processing, distributed computing, and data infrastructure (e.g., **Spark**, **Kafka**, **Beam**, **Flink**)
- Proven technical leadership in cross-functional settings, driving architectural decisions and influencing stakeholders (product, data science, privacy, legal)
- Excellent communication, mentoring, and collaboration skills to align teams on a long-term vision for identity resolution

## Responsibilities

- Lead the technical strategy and architecture for our company’s ads identity modeling solutions and other related ads measurement models
- Design and train advanced ML models while ensuring accuracy, scalability, and compliance with privacy requirements, managing trade-offs between complexity, latency, and prediction quality
- Oversee end-to-end ML workflows—from data ingestion and feature engineering to model training, evaluation, and deployment—optimizing for performance and cost
- Partner with cross-functional teams (e.g., product management, data science, platform engineering, privacy, legal) to define the roadmap and set long-term goals
- Establish engineering best practices, code quality standards, and data governance guidelines to ensure maintainability and trustworthiness of the identity graph
- Mentor and coach junior engineers, fostering a culture of innovation, technical excellence, and knowledge sharing across the organization

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