Staff Machine Learning Engineer, Ads Foundational Representations

New

Reddit UK LimitedAds machine learning

You can work remotely from anywhere on the US East Coast (Eastern Time), Eastern TimeFull-TimeStaff

Salary$230,000 — $322,000 USD

Apply NowOpens the employer's application page

Job Details

Experience: 7+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models.
Required Skills: PythonMachine LearningPyTorchTensorflowNLPComputer Vision

Have 7+ years of hands-on experience designing, training, evaluating, testing, and deploying industry-level models.
Demonstrate Staff-level technical leadership, including mentoring engineers, setting standards, and raising the quality bar.
Have led complex cross-functional projects from requirements and design through alignment and execution without direct people-management authority.
Have a strong track record with content-rich NLP/CV problems at scale and using embeddings to solve them.
Use a data-driven approach to ML system development.
Be familiar with the Ads domain, Search, or recommender systems.
Have experience with PyTorch or TensorFlow.
Preferred: experience with Python, Airflow, BigQuery, Ray, Kubernetes, Kafka, or GCP.
Preferred: hands-on experience using, fine-tuning, or building LLMs.

Provide technical leadership and mentorship to MLEs by driving designs and reviews, establishing best practices, and maintaining quality standards.
Develop technical strategy for content-based embeddings and relevance for Ads with team and organizational leadership.
Develop or iterate on embedding models for advertising use cases, including aggregation pipelines, two-tower architectures, and sequence models.
Use local and third-party LLMs/VLMs to extract representations, develop evaluation methods, prompt-tune, and fine-tune models.
Build data-processing and inference pipelines for models.
Evaluate features qualitatively and quantitatively through internal benchmarks, offline recommender-system metrics, and online experiments.
Improve ML system reliability, scalability, and performance through automated tests, monitoring, and model-management practices.
Review modeling and code work and provide quality and performance feedback.
Collaborate with cross-functional teams to translate business requirements into technical solutions.

View Full Description & ApplyYou'll be redirected to the employer's site