Build and maintain production-grade native iOS applications using Swift and SwiftUI, integrating AI and machine-learning capabilities through backend APIs. Design resilient real-time user experiences, optimize performance and battery usage, collect telemetry, collaborate with ML and backend teams, and support high-quality App Store releases.
Want to build production-grade iOS applications where interactive AI isn't just an afterthought, but the core product experience?
Are you looking for a role where you can bridge native iOS craft with cutting-edge on-device and cloud-based machine learning?
Great, then please read on as we have a role for you.
Our clients mission is to fundamentally change the productivity landscape around email, calendars and note taking by developing proactive, AI-native applications designed for the general public. We are bringing seamless intelligence to workflows, scheduling, and daily errands, all without requiring users to master complex prompting.
As an iOS Software Engineer, you will own the native client experience, shaping how intelligent, data-driven features feel, respond, and perform in users’ hands. This is a core engineering role where interactive AI capabilities are central to the product ecosystem, demanding exceptional mobile performance, rock-solid reliability, and high user trust.
Key Responsibilities
- Build and maintain high-performance native iOS applications using Swift and SwiftUI.
- Integrate intelligent product features seamlessly through backend APIs.
- Architect user experience patterns designed for real-time interactions, including streaming states, progress indicators, retries, and failure recovery.
- Profile and optimize app performance, battery consumption, and memory management.
- Collect user signals and telemetry to support continuous model evaluation and product iteration.
- Partner with ML and backend engineering teams to define API contracts and system architectures.
- Uphold rigorous production quality, release standards, and App Store stability.
To be a good fit for the iOS Engineer role, you will have:
- 3+ years of professional iOS development experience using Swift.
- Practical experience implementing AI-driven or machine learning features within mobile applications.
- Advanced knowledge of Swift concurrency (async/await), memory management, and background processing.
- Demonstrated ability to design robust client experiences around network latency, graceful degradation, and dynamic model responses.
- Track record of shipping and supporting consumer-facing production apps in the App Store.
- Familiarity with on-device ML frameworks (e.g., CoreML) and feature flagging setups.
- Strong product intuition focused on mobile UX and client-side reliability.
Reasons to join:
- Small founding team, building a greenfield AI email product from scratch.
- High‑caliber engineers from top tech companies; very high technical bar.
- Fully remote, global team, strong ownership, and visible impact.
- Very strong reward culture (including significant equity and high comp flexibility
Sounds interesting? Send us your CV by applying to this page
Similar Jobs
Big Data • Food • Hardware • Machine Learning • Retail • Automation • Manufacturing
Lead change management for the S4/o9 transformation across MEU Demand Planning. Partner with senior leaders to design change strategies, assess impacts, deliver training (TNA, curriculum, localization, train-the-trainer), build change capability, manage stakeholder engagement, and track KPIs to drive adoption and measure effectiveness.
Top Skills:
Integrated Business Planning (Ibp)O9 PlanningSap S/4Hana
Cloud • Information Technology • Internet of Things • Machine Learning • Software • Cybersecurity • Infrastructure as a Service (IaaS)
Leads techno-commercial strategy for RAN engagements across EMEA. Translates radio technology into ROI and TCO value propositions, develops pricing and commercial models, supports negotiations, optimizes offer scope, and partners with sales, pricing, contract management, presales, and domain teams. The role drives proactive customer engagement, win rates, margin protection, reusable commercial assets, and profitable growth across complex telecom and ICT deals.
Top Skills:
2G3G4G5GCloud ComputingCloud RanCore NetworksMassive MimoOpen RanOss/BssRanSoftware LicensingTransport Networks
Artificial Intelligence • Big Data • Information Technology • Machine Learning • Database • Analytics
Reconstruct 3D power grid networks using LiDAR point clouds, GIS maps, and drone imagery. Place and align infrastructure objects, synchronize 2D maps with 3D models, and annotate aerial images to identify grid components and structural defects. The role requires strong spatial reasoning, attention to detail, English communication skills, and suitable computer hardware. Prior LiDAR or power-grid knowledge is advantageous. This is a fully remote, full-time position.
Top Skills:
3D ModelingAIDigital TwinsDrone ImageryGisLidarPoint Cloud Data
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
