Senior Software Engineer

Traackr is a global SaaS technology company providing a data-driven influencer marketing platform that marketers use to optimize investments, streamline campaigns, and scale programs. Our customers range from some of the world’s largest companies in the beauty and personal care space to digitally native indie brands, which have all made influencer management and engagement a critical practice of their marketing and advertising programs.

We are a remote-first company, and for the folks that like to meet in person, we have offices in San Francisco, New York, Boston, Paris, and London.

At Traackr, we’re lucky to have a team of kind, driven, and respectful humans from around the world. We operate on a culture of mutual respect, with core value pillars including:

• Trust
  . We earn the trust of our team, customers, creators, and partners through transparency, predictability, and integrity.
• Diversity
  . Bringing diverse perspectives to the table results in stronger outcomes. All are welcome.
• Value
  . Through our words and actions, we strive to create tangible value for our customers and peers. We only succeed when our community succeeds.
• Ownership
  . We lead with action. We take pride in solving the hardest challenges and feel accountable for our commitments.
• Mutual success
  . We share goals with each other and with our clients. Alignment, collaboration, and empathy are the cornerstones of our success.

Our team also loves having fun together, with many employee-led initiatives like the Traackr Summer Olympics annual workout challenge!

As a Senior Software Engineer, you will make a major impact on Traackr’s scalable influencer marketing platform by designing and evolving our distributed backend systems: data processing pipelines, API integrations, and product-facing services. You’ll own work end-to-end, from framing the problem and defining success metrics to shipping safely and operating reliably in production. In an AI-enabled engineering era, you’ll use GenAI tools to accelerate delivery, and apply an experimentation mindset to validate outcomes: versioning and evaluating changes (including prompts/config), measuring impact with data, and building observability and regression checks that keep quality high as systems evolve.

• Own backend features end-to-end: discovery, design, implementation, rollout, and long-term reliability/operations
• Design and evolve distributed systems (services, pipelines, data stores) with strong performance, scalability, and resiliency
• Build and maintain APIs (REST/Graph
  
  QL, where relevant) and data access patterns supporting analytics and search use cases
• Establish strong engineering fundamentals: code reviews, testing strategies, incident readiness, and operational excellence
• Apply an experimentation-first approach: define hypotheses, define success metrics/guardrails, run controlled rollouts and A/B tests when appropriate, and write clear readouts for stakeholders.
• Bring evaluation discipline to AI-assisted work: treat prompts/configs like versioned artifacts, design regression tests, measure quality changes, and monitor for drift (the same way you would for performance or correctness)
• Use GenAI productively and responsibly (for coding assistance, debugging, and design reviews), while maintaining high standards for correctness, security, and privacy.
• Mentor teammates and raise the bar through documentation, pairing, and lightweight coaching on best practices

• 5+ years building and scaling backend systems (distributed systems experience expected at senior level)
• Strong JVM fundamentals; ability to troubleshoot and performance-tune production services
• Production experience with Kotlin (functional/reactive patterns a strong plus)
• Strong SQL + data modeling skills (Postgre
  
  SQL; familiarity with jOOQ or similar)
• Experience with event-driven architectures (Kafka) and cloud deployment on AWS; containers (Docker/Kubernetes)
• Strong communication skills and ability to collaborate across functions
• Comfort with AI-assisted engineering and the discipline to validate outcomes (tests, metrics, evaluation) rather than trusting outputs blindly

• Search/retrieval systems…