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Hang

Machine Learning Engineer

Los Angeles, California, United States

Location
Los Angeles, California, United States
Work
Remote
Employment
Full-time
Level
Senior
Function
Data & AI
Pay
Not stated
Posted
36 months ago

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141 data jobs open in Los Angeles: 14% remote, 34 posted in the last 30 days.

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About the role

Hang is building the future of loyalty for brands.

Hang is the next generation brand loyalty & membership platform. By harnessing the power of personalization, gamification, and its integrations ecosystem, Hang provides brands with a radically new type of loyalty experience for their customers.

Today, they work with a variety of major brands (such as Ulta Beauty, Budweiser, Flipkart, and more), as well as multiple well-known, up-and-coming restaurant chains (Boba Guys, Roam Artisan Burger, and Williamsburg Pizza, among several others).

Hang draws from years of deep expertise in loyalty, game design, and finance with employees from leading companies like Google, Amazon, Apple, Meta, LinkedIn, Coinbase, Square, and Goldman Sachs.

Hang raised a $16 million Series A led by Paradigm last summer, with participation from Tiger Global, Howard Schultz, Kevin Durant, Mr. Beast, and the founders of Warby Parker, Allbirds, and Bombas, among others.

About the Role

We are seeking a skilled and innovative Machine Learning Engineer to join our team. This person will implement and develop machine learning models to enhance our platform's capabilities, making key contributions to our product development, and driving data-driven decision-making.

What You’ll Do

  • Model Development: Design, build, and deploy machine learning models to improve various aspects of our platform, including customer personalization, predictive analytics, and automated decision-making.
  • Data Analysis: Analyze large datasets to identify trends and patterns, and use this information to inform model development and business strategies.
  • Algorithm Optimization: Continuously test and refine algorithms to improve accuracy and efficiency.
  • Collaborative Development: Work closely with software engineers, data scientists, and product managers to integrate ML models into our platform and ensure seamless deployment.
  • Research and Innovation: Stay up-to-date with the latest developments in machine learning and AI, and explore new techniques and technologies that could benefit Hang.
  • Technical Leadership: Provide insights and guidance on best practices in machine learning, and contribute to the strategic direction of our technology.

Who You Are

  • Bachelor's or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. A Ph.D. is a plus.
  • 5+ years of experience in machine learning and data related roles.
  • Proven experience as a Machine Learning Engineer or in a similar role.
  • Strong programming skills in Python and familiarity with ML frameworks (like TensorFlow or PyTorch).
  • Experience with data processing and data analytics.
  • Experience with vector databases, demonstrating proficiency in managing and querying high-dimensional data. Familiarity with popular vector databases like Redis, Milvus, Pinecone, Weaviate, Chroma or Faiss is required.
  • Knowledge of machine learning techniques and algorithms, including fine tuning processes and methodology.
  • Experience in deploying Large Language Models (LLMs) in production environments, including optimization and scaling considerations.
  • Experience with snowflake, Postgres, RDS, Redis and AWS.
  • Excellent problem-solving skills and ability to work in a fast-paced environment.
  • Strong communication skills and ability to work well in a team.
  • Experience with Ruby is a plus.

What Would Set You Apart

  • You have a passion for consumer brands and loyalty programs.

Benefits

  • Top-tier health, vision, and dental insurance, including plans with $0 employee cost.
  • Unlimited PTO / sick leave
  • Competitive salary & equity compensation.
  • Quarterly company offsites

Source

Posted by Hang on their own hiring page, jobs.ashbyhq.com. OptiTalent shows it as published, read 07 Nov 2026 12:10 UTC; the original is the reference.

View the original ad ↗

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