Senior Machine Learning Engineer, Ads Foundational Representations

EngineersOfAI; United Kingdom; Hybrid; GBP 65,000 - 85,000; Full time

Job summary

EngineersOfAI is seeking a Senior ML Engineer to lead full-cycle execution of ML projects. This role involves developing embedding models, working with LLMs/VLMs, and building data processing pipelines. Ideal candidates will have solid experience in machine learning and experimentation methodologies.

The Senior ML Engineer will also ensure scalability and reliability of ML systems, making significant contributions to the Ads Foundational Representations team.

Qualifications

• Experience in full-cycle execution of ML projects.
• Proficient in developing and iterating on embedding models.
• Ability to work with various ML frameworks and tools.

Responsibilities

• Develop and iterate on existing embedding models.
• Work on local and 3rd-party LLMs/VLMs.
• Build data processing and inference pipelines.
• Conduct qualitative and quantitative evaluations.
• Ensure reliability and scalability of ML systems.

Skills

Machine Learning

Data Processing

Embedding Models

LLM Fine-tuning

Experimentation

Job description

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:

• Multimodal & Content Embeddings - Make sense of organic (posts, comments, subreddits) and promoted (ads, shopping products, their landing pages) text and media content by embedding them into a shared space.
• Contextual and Behavioral Relevance - Working with Product & Data Science, establishing definitions of what ads are relevant to users and the content we show them next to, building metrics and fine‑tuning embeddings to better reflect relevance.
• Knowledge Graph Embeddings - Building representations for the Knowledge graph entities, e.g., intellectual properties/brands, to be used for high-precision targeting & business insights.
• User Intent Modeling - Leveraging various techniques to introduce user representations based on the content they interact with: batch & real-time sequence modeling, LLM summarization, etc.
• LLM-based Representations - Leveraging LLMs, VLMs, and foundational models to build complex representations of Reddit entities that improve ranking outcomes.

The signals and features we create become a key piece in the Ads Delivery funnel, from targeting to the auction, as well as the Business Insights product and other advertiser-facing products such as Creative generation and optimization.

As a Senior ML Engineer, you’ll be in charge of the full‑cycle execution of ML projects - from collaborating with cross‑functional teams on requirements and design, to the implementation of the feature and its experimentation.

Responsibilities

• 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.