Senior Machine Learning Engineer, User Signal & Ads

Founded in 2015, NewsBreak is the Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech.

Recognized by Fast Company as #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale.

Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence.

If you’re inspired to dream big, innovate fast, and make a difference, we’d love to hear from you! For more information, visit www.newsbreak.com/about

The User Signal team sits at the heart of our in-house advertising platform. We collect, process, and activate user signals — behavioral events, contextual data, engagement history, and identity signals — and turn them into the features and audiences that power ads targeting, bidding, and ranking across the company's ad stack.

The quality of our signals directly determines how well every downstream ML model performs: better signals mean better targeting, higher CTR/CVR, and stronger monetization. We own the full lifecycle — from raw event ingestion and large-scale feature pipelines, to identity-prediction models, embeddings, and online serving — and we close the loop by applying those outputs inside the ads models that consume them in real time.

We are looking for an experienced Senior Machine Learning Engineer to design and build the data and ML systems that transform raw user signals into production targeting and bidding features. This is a hands-on, high-ownership role that blends ML modeling, large-scale data engineering, and production ML systems.

You will own significant pieces of the signal-to-model pipeline end to end: defining and building features at scale, developing models for user understanding — most importantly identity prediction — and then applying those predictions directly inside the ads targeting, bidding, and ranking models to drive measurable lift. You won't just hand features off to a downstream team; you'll close the loop, ensuring everything is served online with the freshness, latency, and reliability that real-time bidding demands. You'll partner closely with Ads, Data, and Infra teams, and you'll be expected to drive technical direction — not just execute well-scoped tasks.

• →Build user-signal features at scale: design, implement, and own offline and online feature pipelines (batch + streaming) that turn raw user events into high-quality targeting and bidding features.
• →Develop user-understanding models — especially identity prediction: build and improve models such as identity prediction, user/content embeddings, intent and conversion prediction, and signal-quality / value models that feed ads targeting and bidding.
• →Apply model outputs inside ads models: integrate identity-prediction results and other user signals directly into the ads targeting, bidding, and ranking models — owning the impact all the way to revenue, not just the upstream features.
• →Own the model lifecycle: data preparation, feature engineering, training, offline/online evaluation, deployment, monitoring, and iteration — with rigorous A/B testing and clear business metrics (CTR, CVR, ROAS, revenue).
• →Bridge modeling and serving: ensure features and model outputs are available online with the freshness and low latency required by high-QPS real-time bidding, working across feature stores, embedding stores, and serving infra.
• →Improve signal quality and coverage: identify gaps, biases, and freshness issues in user signals; build the data quality, labeling, and validation systems that keep features trustworthy.
• →Collaborate cross-functionally with Ads ranking/bidding, Data, and Platform teams to align signal and feature design with downstream model and business needs.
• →Provide technical leadership: drive design reviews, set best practices for ML and feature engineering, mentor engineers, and raise the quality bar for the team.

• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related quantitative field (or equivalent practical experience).
• 5+ years of industry experience as an ML engineer / applied scientist, building and shipping ML models in production (not just research or offline prototyping).
• Strong foundation in machine learning: feature engineering, supervised learning, embeddings/representation learning, and offline + online evaluation methodology.
• Proficiency in Python and a solid ML stack (e.g. PyTorch or TensorFlow, scikit-learn, pandas/NumPy).
• Hands-on experience with large-scale data processing for ML — e.g. Spark, Flink, SQL/Presto/Trino — including building production feature or training-data pipelines.
• Experience taking models from idea to production: training, deployment, monitoring, and iterating based on real metrics and A/B tests.
• Strong analytical and problem-solving skills, and the ability to reason about model behavior, data quality, and business impact end to end.

• Direct experience in Ads, recommendation, search, or growth ML — especially targeting, bidding, ranking, CTR/CVR prediction, or identity prediction.
• Experience with user-signal / behavioral data: event pipelines, identity prediction, user embeddings, and applying model outputs back into downstream ranking/bidding models.
• Familiarity with online feature serving — feature stores, embedding/vector stores, and low-latency, high-QPS inference for real-time bidding.
• Experience with streaming systems (Kafka, Flink, Spark Streaming) for real-time feature computation.
• Knowledge of the big-data ecosystem (Hadoop, Spark, Hive, Presto/Trino) and modern ML platforms / MLOps tooling (training orchestration, experiment tracking, model registries, feature stores).
• Experience with large-scale / distributed model training and inference optimization (e.g. distributed training, embedding tables, quantization, efficient serving).
• A track record of measurable business impact (revenue, ROAS, CTR/CVR lift) from ML work, and of driving technical direction across teams.