As Technical Lead, Applications, you directly own the core application architecture that connects AI systems to real-world user experiences. You are responsible for the design, reliability, and evolution of backend services, APIs, and client integrations across mobile and desktop.
This is a hands-on leadership role. You stay close to the code, make critical architectural decisions, and ensure the system remains fast, reliable, and coherent as it scales.
Focus
- Own the application engineering strategy and execution across backend, mobile, and desktop.
- Lead and grow a small, senior applications team, including backend, mobile, and desktop engineers.
- Ensure production-grade reliability: observability, monitoring, retries, fallbacks, and failure handling.
- Set the architectural direction for AI-powered product workflows, APIs, and client integrations.
- Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes.
- Partner closely with Machine Learning leadership to translate model capability into shippable product features.
- Make high-impact decisions across latency, cost, reliability, security, and user experience.
- Establish a strong execution culture through code reviews, design reviews, and hands-on technical leadership.
- Ensure production readiness: observability, monitoring, retries, fallbacks, privacy, and cost controls.
- Balance speed and discipline – shipping quickly without compromising long-term system quality.
Ideal Experiences
- Significant experience leading application or product engineering for complex systems.
- Strong hands-on background building and shipping backend systems used in production.
- Experience delivering AI-powered products, with a deep understanding of model integration trade-offs.
- Excellent system design skills across services, APIs, clients, and data flows.
- Comfort operating across platforms (backend, mobile, desktop), even if your depth is in one.
- Proven ability to make high-impact technical decisions under ambiguity.
- A strong bias toward shipping, iteration, and learning from real-world usage.
- Move fast without breaking fundamentals
Outcomes
- Production systems are observable, monitored, and stable under real-world load.
- Decisions consistently balance speed, cost, reliability, and user experience.
- Features across backend, mobile, and desktop work smoothly and reliably.
- Latency and reliability improve measurably over time.
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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