Staff Machine Learning Engineer - Ads Foundational Representations
R
RedditAdvertising technology
You can work remotely from anywhere in the UK or the NetherlandsFull-TimeStaff
Salary not disclosed
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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
Requirements
- 7+ years of hands-on experience designing, training, evaluating, testing, and deploying industry-level models.
- Demonstrated Staff-level technical leadership, including mentoring engineers, setting standards, and leading complex cross-functional projects from requirements and design through alignment and execution.
- A strong track record working on content-rich NLP/CV problems at scale and using embeddings to solve them.
- A data-driven approach to machine learning system development, including working directly with data and examining metric results.
- Familiarity with the Ads domain and/or Search or Recommender systems.
- Experience with PyTorch or TensorFlow.
- Preferred: experience with Python, Airflow, BigQuery, Ray, Kubernetes, Kafka, and GCP.
- Preferred: hands-on experience using, fine-tuning, or building LLMs.
Responsibilities
- Provide technical leadership and mentorship to MLEs, drive design reviews, and establish best practices in analysis, modeling, and engineering.
- Develop technical strategy for content-based embeddings and relevance for Ads with team and organizational leadership.
- Develop and iterate on embedding models for advertising use cases, including aggregation pipelines, two-tower architectures, and sequence models.
- Use local and third-party LLMs and VLMs to extract representations, develop evaluation methods, and prompt-tune or fine-tune models.
- Build data-processing and inference pipelines for machine learning models.
- Evaluate features through internal benchmarks, recommender-system offline metrics, and online experiments.
- Improve ML system reliability, scalability, and performance with automated tests, monitoring, and model-management practices.
- Review modeling and code and provide feedback on quality and performance.
- Collaborate with cross-functional teams to translate business requirements into technical solutions.
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