• Job Description

  Req#: aabf7b10-4cb8-49fa-b35f-1572434f0964

  About Citizen

  Citizen is the #1 personal safety app in the U.S. with a mission to Protect the World and make communities safer. We believe safety is a shared responsibility and we’re building a global network where people help protect one another, creating a safety net that benefits everyone.

  Citizen provides free real-time public safety alerts to millions of users in 76+ North American markets and earns revenue through consumer subscriptions and enterprise solutions. Citizen’s investors include 8VC, Greycroft, Goodwater, Lux, Founders Fund, and Axon.

  About the Role
  We are seeking an experienced Machine Learning Engineer to drive the development of core recommendation capabilities that deliver real-world relevance and value to our users. You will work closely with our video team to build systems that process large volumes of real-time video content — transforming noisy, user-generated media into a compelling and trustworthy feed experience.
  You will lead major initiatives such as:

  • Real-time ranking and recommendation of newly uploaded videos

  • Modeling video relevance, quality, and local importance

  • Noise and duplicate detection to reduce feed clutter

  • Leveraging multimodal signals (visual + audio + metadata) for relevance scoring

  • Building human-in-the-loop feedback loops that continuously improve model quality

  You’ll drive prioritization and technical execution across ML efforts, identify opportunities for emerging AI/ML methods, and ensure that our recommendation models scale to support rapid product and user growth. If you are passionate about building ML systems that meaningfully improve user safety, this role is for you.Our Challenges
  Scaling & Distribution
  We serve one of the fastest-growing user bases in public safety, expanding rapidly across major cities and preparing for nationwide scale.
  Bursting
  During major events, hundreds of thousands of simultaneous clients rely on us for timely content, including live video streams — requiring dynamic scaling and robust ranking under load.
  Machine Learning
  We process thousands of user-submitted videos daily and must:

  • Detect highly relevant and urgent content quickly

  • Rank and personalize feeds under tight latency constraints

  • Actively combat noise, redundancy, and cold-start challenges

  Our Stack — languages and technologies we use and teach

  • Services: Go for real-time systems; Python for ML

  • Data: Cassandra, MySQL, Redis, Google PubSub

  • Infra: Kubernetes on Google Cloud; deep utilization of OpenAI platforms plus growing internal ML capabilities

  Preferred Qualifications

  • Degree in CS, Machine Learning, or equivalent industry experience

  • Deep understanding of applied ML pipelines in production environments

  • Experience across ML modeling techniques and frameworks (e.g., ranking, embeddings, neural networks, tree-based models)

  • 5+ years building and shipping ML systems with measurable product impact

  • Bonus: Experience in recommender systems, video understanding, or human-in-the-loop learning

  Salary Range

  This role offers a base salary of $200,000 - $225,000 per year, plus equity.

  Compensation & Benefits
  We offer competitive salary, equity, and a full benefits package, including:

  • At least one fully employer-sponsored medical plan option

  • Dental and vision coverage, with a small employee contribution

  • Unlimited PTO

  • Up to 14 weeks paid parental leave (for all parents)

  • Monthly tech stipend

  • Free mental health support

  • Hybrid work environment + catered lunches (NYC HQ)

  Diversity, Equity & Inclusion at Citizen
  Citizen is an equal opportunity employer. We’re building a team that reflects the diversity of the communities we serve — across race, gender identity, sexual orientation, age, ability, and background.

  We know that diverse teams make better decisions and build more effective products. If you don’t meet every listed qualification, we still encourage you to apply. Skills, potential, and lived experience matter.

• About the company

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