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Tatari

Senior Backend Engineer

New York, United States

Location
New York, United States
Work
Hybrid
Employment
Not stated
Level
Senior
Function
Engineering
Pay
$170K – $200K a year (read from the ad)
Posted
4 months ago

About the role

Tatari is on a mission to revolutionize TV advertising. Founded in 2016 to help transform the antiquated world of TV advertising through the intelligent application of AI and machine learning, Tatari helps some of the world’s fastest growing brands including Chime, Calm, Tecovas, Manscaped, Saatva, and Liquid I.V., reach their customers using linear and streaming TV ads. Our platform combines sophisticated media buying with proprietary analytics to turn TV advertising into an automated, digital-like experience, enabling businesses of any size to advertise on TV.

That approach has earned Tatari broad industry recognition, including being named Best CTV AdTech Platform in the 8th annual MarTech Breakthrough Awards, as well as honors from Digiday (Best Connected TV Platform), AdExchanger (Most Innovative TV Advertising Technology), and Business Insider (Hottest AdTech Companies). Tatari has also been recognized as the Best Place to Work by Inc. Magazine. Backed by an executive team of former founders and senior leaders from companies including Shazam, TrueCar, AdapTV, LiveRail, Amazon, Google, Meta, Microsoft, and Yahoo, Tatari continues to scale rapidly as TV advertising enters its next major era.

We are looking for Senior Backend Engineers to join Tatari and play a key role in the evolution and scaling of our platform. In this role, you will contribute to system architecture decisions, mentor junior team members, promote best practices, and ship high-quality code to production daily. At Tatari you are empowered to do your best work at a sustainable pace with a stellar group of smart, creative, and humble human beings. Come help us build a television first programmatic buying experience!

Responsibilities

  • Architect and own the development of new features
  • Collaborate with tact and lead with empathy across the organization on system architecture, technology, and scaling decisions
  • Mentor more junior members of the team
  • Design relational and non-relational data models for optimal storage, retrieval, and complex processing/calculations
  • Drive technical architecture discussions and peer code reviews
  • Collaborate effectively with Data Science, Product, and across engineering teams to push boundaries, shape our roadmap, and deliver on our mission

Qualifications

  • 6+ years developing software with object-oriented and/or functional programming languages - Python required
  • Hands-on experience with agentic coding tools (e.g., Cursor, Copilot, Cody) to accelerate development while maintaining a high quality bar
  • Experience evaluating and deploying LLM-based systems in production (latency, cost, reliability trade-offs)
  • 4+ years building distributed systems at scale
  • 4+ years with a modern data stack (RDS, Databricks, Spark, Redshift, Kafka, Airflow, lakehouse architectures, real-time databases, dbt, etc.)
  • Deep SQL and data modeling expertise
  • Experience working across the organization delivering on large impact initiatives
  • Nice to have:
  • Experience developing highly available API using modern frameworks (e.g. REST, gRPC, GraphQL)
  • Experience developing applied AI/ML-enabled features
  • Familiarity with prompt engineering, RAG architectures, evaluation frameworks, or fine-tuning

Benefits

  • Total compensation ($170,000 - $200,000)
  • Equity compensation
  • Health insurance coverage for you and your dependents
  • 401K, FSA, and commuter benefits
  • $150 monthly spending account
  • $1,000 annual continued education benefit
  • $500 Newbie Productivity Perk
  • Unlimited PTO and sick days
  • Monthly Company Wellness Day Off
  • Snacks, drinks, and catered lunches at the office
  • Team building events
  • Hybrid RTO of 2 days per week in office

Source

Posted by Tatari on their own hiring page, job-boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 03:17 UTC; the original is the reference.

View the original ad ↗

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