Jobs at NewsBreak

Design and develop machine learning infrastructure, enhancing core components for model training, monitoring, and serving. Collaborate with ML engineers on robust pipelines.

Build and own an agentic creative pipeline that generates, vets, and optimizes thousands of image and video ad variants daily: extend LLM critic feedback to multimodal assets, build fast cost-efficient video pipelines, route generative models by cost/quality, train and deploy brand-style checkpoints, implement policy pre-flight checks, and ship event-triggered ad pipelines with A/B validation in production.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

Manage a portfolio of agency clients as the primary contact: lead onboarding and campaign launches, run performance reviews and QBRs, drive scale and retention, diagnose and save at-risk accounts, educate agencies on ad products, and collaborate with ad ops, product, and sales to resolve technical issues and shape product roadmaps.

Manage a portfolio of eCommerce performance advertisers to drive revenue growth through campaign optimization, scale, and product adoption. Analyze performance data, run QBRs, troubleshoot tracking integrations (pixels, SDKs, server-to-server), and partner with Product, Engineering, and Ad Ops to improve platform features and client outcomes.

Manage a portfolio of performance advertisers in the App vertical to drive revenue growth through campaign optimization, scaling spend, and client adoption of ad products. Provide data-driven optimizations, conduct QBRs, troubleshoot tracking/integration issues, and partner cross-functionally to influence product and platform improvements.

Build and deploy ML systems for search, recommendation, ranking, retrieval, matching, and personalization. Work on query understanding, embeddings, NLP/LLMs, end-to-end ML pipelines, experimentation, and monitoring to improve relevance, engagement, conversion, and marketplace efficiency in a consumer local-search product.

Build and productionize ML models and pipelines for search, recommendation, ranking, retrieval, matching, and personalization. Apply NLP, embeddings and LLMs for query understanding and semantic retrieval. Partner with product, engineering, and data teams to define problems, run experiments, and measure impact to improve relevance, engagement, conversion, and marketplace efficiency.

Build and manage end-to-end sourcing pipelines for software engineering roles using recruiting tools. Craft outreach, conduct phone screens, partner with recruiters and hiring managers, maintain ATS data, produce talent maps and sourcing metrics to optimize pipeline conversion.

Provide high-touch executive support including complex calendar management, travel planning, meeting preparation, expense processing, and project tracking. Serve as primary liaison to leadership and coordinate team events. Support HR tasks such as interview scheduling, onboarding logistics, benefits administration, and immigration case coordination. Maintain discretion, streamline processes, and support cross-functional initiatives.

Design and build search-ranking, two-sided marketplace matching, retrieval/recall, and dispatch models. Own end-to-end learning-to-rank pipelines, run causal experiments (A/B, switchback), apply modern ML/LLM methods, and optimize relevance to improve marketplace conversion at production scale.

Build and scale NewsBreak's local knowledge graph: POI/places schema and ontology, entity resolution/linking, multi-source POI fusion, and data-quality systems. Ensure graph accuracy over time and partner with intent and matching teams to power search, discovery, and matching.

Own a local commerce/local services vertical end-to-end: define strategy, roadmap, launch, and growth with P&L ownership. Lead 0→1 category validation, MVP, and go-to-market (supply acquisition, demand, pricing, liquidity). Partner with engineering and data science to embed AI/LLM features and use marketplace metrics to prioritize and measure impact.

Build and ship 0→1 growth mechanics for AI products: code acquisition funnels, onboarding, referral/viral loops, activation triggers, and monetization experiments. Instrument lightweight analytics and A/B testing, run experiments, measure traction, and hand off successful mechanics to product teams.

The role involves leading technical directions for recommendation systems and AI initiatives, driving adoption of AI technologies, and mentoring others in system-level thinking.

The Senior Machine Learning Engineer will enhance recommendation systems, employ AI/LLM technologies, and drive AI innovation for content discovery and user engagement.

Design and build an AI-driven account management and optimization platform using LLMs and agentic systems. Create diagnosis and recommendation models, ensure reliable long-running services with observability and safeguards, and collaborate with ad delivery and product teams to improve advertiser performance and revenue.

Serve as a strategic talent partner advising executives on hiring strategy, organizational design, and workforce planning. Lead full-cycle recruiting for engineering, AI/ML, infrastructure, and leadership roles while improving recruiting operations, sourcing automation, and candidate experience. Coach managers across the employee lifecycle and use talent intelligence to influence business decisions.

Build and maintain backend and data pipelines powering ads, personalization, and retention systems. Contribute to recommendation algorithms, feature pipelines, experimentation infrastructure, revenue operations tooling (commission/quota), and observability for large-scale user and engagement data. Collaborate with product, sales, and finance to ship reliable, low-latency production systems and monitoring.

Lead post-training for LLMs (CPT, SFT, RL) focusing on RL methods; design and curate training and reward data; run and debug distributed training on mid-to-large GPU clusters; build evaluation and verifier pipelines; partner with product and business teams to deliver targeted model improvements quickly.