NewsBreak

NewsBreak

Senior Machine Learning Engineer, User Signal & Ads

Bellevue, Washington, United States · Senior · Full-time

Sponsorship not specified$185k-$235kDetected 34 days ago
PythonSQLVector DatabasesKafkaMachine LearningTensorFlowPyTorchscikit-learnPandasNumPySparkData EngineeringMLOpsStatisticsA/B TestingResearchLeadershipCollaborationProblem Solving

About the role

  • This is a hands-on, high-ownership role that blends ML modeling, large-scale data engineering, and production ML systems.
  • 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.

Responsibilities

  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • The chance to own a mission-critical part of the ads stack - the user signals and features that every targeting and bidding model depends on.
  • At NewsBreak, we design our overall rewards package to attract top talents.

Requirements

  • 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.
  • 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.

Nice to have

  • 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.

Compensation

  • The US base salary range for this full-time position is listed below.
  • Annual Base Pay Range
  • $185,000 - $235,000 USD

Benefits

  • Competitive compensation and benefits.
  • We offer a competitive benefits package:
  • Health, dental, and vision care for you and your family (100% coverage for employee)
  • Paid time off and paid holidays
  • FSA, HSA and commuter benefits programs
  • Depending on the position, the role may also be eligible for discretionary bonus and options.
  • Improve signal quality and coverage: identify gaps, biases, and freshness issues in user signals
  • Pay may vary based on a number of factors including job-related skills, level, experience, geographic location and relevant education or training.

Company info

  • About NewsBreak
  • 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
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This listing is sourced directly from NewsBreak's careers page and normalized into a canonical job model.