Senior Machine Learning Engineer, Ads Foundational Representations
New
R
RedditAds Engineering
You can work remotely from anywhere in the UK or the Netherlands.Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- PythonMachine LearningPyTorchTensorflowDeep LearningNLPComputer Vision
Requirements
- 7+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models.
- Demonstrated Staff-level technical leadership: mentoring engineers, driving standards, and leading complex cross-functional projects.
- Excellent communication skills for translating complex technical concepts.
- Strong track record of working on content rich NLP/CV problems at scale using embeddings.
- Established data-driven approach for ML system development.
- Familiarity with the Ads domain and/or Search/Recommender systems.
- Experience with mainstream DL frameworks: PyTorch or TensorFlow.
- Preferred: Experience with stack (Python, Airflow, BigQuery, Ray, k8s, kafka, GCP).
- Preferred: Tech leadership experience mentoring junior engineers.
- Preferred: Hands-on experience with using/fine-tuning/building LLMs.
Responsibilities
- Provide technical leadership and mentorship to MLEs in the team: driving designs & their review, establishing best practices in analysis, modeling and engineering.
- Work closely with team/org leadership developing technical strategy for content-based embeddings & relevance for Ads.
- Develop new or iterate on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models.
- Work with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings.
- Build data processing and inference pipelines for the models developed.
- Conduct qualitative and quantitative evaluation of features, including end-to-end experimentation from internal benchmarks to downstream recommender system metrics.
- Ensure reliability, scalability, and performance of ML systems through automated testing, monitoring, and model management best practices.
- Participate in modeling and coding reviews to ensure quality and performance standards.
- Collaborate with cross-functional teams to translate business requirements into technical solutions.
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