Senior Machine Learning Engineer, Ads Foundational Representations
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Compensation
Salary & market context
Salary not listed
Requirements
Top requirements
- s:
- 5+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models.
- Experience building NLP or CV models and integrating them at scale.
- Experience developing complex features/embeddings for downstream models.
Perks & setup
Benefits candidates care about
- s:
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
Why candidates care
Benefits & perks
- s:
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Private Pension plan with Employer-matching
- 100% employer-sponsored group medical plan
- Income Replacement Programs
Start here
Requirements
- s:
- 5+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models.
- Experience building NLP or CV models and integrating them at scale.
- Experience developing complex features/embeddings for downstream models.
- Experience with mainstream DL frameworks: PyTorch or TensorFlow.
- Excitement about working with data and readiness to look behind the metric numbers.
Responsibilities
What you'll do
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- Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models.
- Working 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.
- Building data processing and inference pipelines for the models we develop.
- Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments.
- Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management.
- Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance.
- Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.
Role snapshot
About the role
Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .
Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the UK or the Netherlands.
The Ads Foundational Representations (AFR) team develops signals and representations of Reddit’s core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs. We work on building embeddings to understand content and users' interests based on the content they engage with.
Our team has the potential to highlight one of Reddit's biggest differentiators: genuinely curated, high-quality, extremely relevant, and daily updated organic content. We are a Machine Learning/Data heavy team with a focus on the following areas:
More detail
Nice to have
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- Experience with our stack (Python, Pytorch, Airflow, BigQuery, Ray, k8s, kafka, GCP)
- Familiarity with the Ads domain and/or Search/Recommender systems is a strong plus.
- Tech leadership experience: mentoring junior engineers and leading complex projects.
- Hands-on experience with using/fine-tuning/building LLMs.
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