Cantina

• Architect, implement, pre-train, fine-tune, and post-train/alignment large-scale speech models • Design, run, and analyze scientific experiments to advance understanding of models • Develop and improve development tooling to enhance team productivity • Contribute across the stack, from low-level optimizations to high-level model design • Define data requirements and collaborate on acquisition, curation, augmentation, labeling quality, and synthetic data strategies • Design automated objective and subjective evaluations, including listening tests, SV/WER/ASR-based metrics, robustness and bias checks, and red-team studies • Harden the training-to-evaluation-to-inference pipeline; profile latency, memory, and cost; meet production SLAs with monitoring and rollback • Contribute to safety and consent guardrails and misuse/abuse mitigation for responsible speech technology • Partner with research, data, infrastructure, and product teams to ship measurable improvements to speech systems

• Establish a foundation that accelerates product development by building reusable, scalable, high-quality components • Design and build core services and product platform components, including entities and relationship systems, for use across the engineering organization • Develop and maintain search infrastructure, including search indexes and relevance systems • Oversee DevOps initiatives, including AWS usage, Terraform configurations, infrastructure upgrades, and platform observability • Optimize the backend repository and Go stack to improve developer iteration speed • Provide consulting and mentorship to engineering teams • Promote best practices and guide adoption of new platform technologies • Lead cross-cutting initiatives and consult on architectural decisions

• Audio Representations: Design, train, and improve the audio VAEs, neural codecs, and vocoders our generative models sit on top of latent design, reconstruction and perceptual objectives, compression-vs-fidelity tradeoffs. • Model Building: Architect, implement, pre-train, fine-tune, and post-train/alignment (e.g., GRPO/DPO) diffusion and flow-matching transformers for large-scale audio and video generation. • Joint Audio-Video Modeling: Design the audio conditioning and cross-modal alignment inside joint AV models, audio latents alongside video latents, reference-audio and multi-speaker conditioning, multi shot generation audio/video modeling. • Experimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models. • Data Ownership: Define data requirements and collaborate on acquisition, curation, AV-sync and quality filtering, annotation quality, and synthetic data strategies for paired audio-video and speech corpora. • Rigorous Evaluation: Design automated objective/subjective evaluations audio fidelity and intelligibility metrics, AV-sync, listening and viewing tests, robustness & bias checks, and red-team studies. • Inference Efficiency: Drive distillation, step-count reduction, quantization, and kernel/memory optimization to meet interactive latency and cost targets. • Pipeline Delivery: Harden the training → evaluation → inference pipeline; profile latency, memory, and cost; and meet production SLAs with robust monitoring and rollback. • GPU Scaling: Partner with infrastructure to run distributed training/inference on cloud fleets and productionize models with reliability and observability. • Project Leadership: Independently lead small research projects while collaborating on larger team initiatives, including cross-team work with video generation. • Tool Development: Develop and improve dev tooling to enhance team productivity. • Safety & Responsibility: Contribute to safety/consent guardrails, watermarking, and misuse/abuse mitigation for responsible voice and likeness technology.

• Design and maintain inference infrastructure for generative audio model architectures. • Implement and manage high-performance inference engines. • Orchestrate service deployments using Kubernetes (K8S), implementing advanced autoscaling paradigms to handle varying traffic loads efficiently. • Develop and automate robust CI/CD pipelines to streamline the testing and deployment of model artifacts and inference configurations. • Monitor production systems, establishing observability practices to track latency, resource utilization, and overall model performance. • Collaborate closely with research teams to optimize model serving paths and evaluate various inference strategies. • Optimize inference performance for both streaming and batch applications.

• Produce and direct scaled creative across all performance marketing surfaces (Meta, TikTok, YouTube, mobile ad networks, etc) with multi-million dollar ad spend. • Develop performance creative concepts built on intimate understanding of Cantina as a product/platform and its consumer use-cases. • Build and own processes for producing high volume, iterative ads leveraging both traditional and AI-generated production. • Define creative strategies and vision for AI-generated ads. • Own creative performance analytics to track what's working and iterate on best-performing concepts and campaigns. • Set the creative inputs across creative testing plans to optimize creative. • Manage external agencies and cross-functional teams to ensure high-quality creative output across all performance marketing channels. • Lead regular creative brainstorms, reviews, and presentations to internal stakeholders, including leadership. • Oversee the QC process for in-house and external creatives: Create scalable creative review processes and quality standards that maintain excellence at high volume. • Stay up-to-date on and apply emerging technologies, production methods, and best practices in performance creative and performance marketing.

Role Description As a Kotlin Multiplatform Engineer at Cantina, you’ll be the architect of our shared-code strategy — building the foundation that powers our experiences across Android, iOS, and web from a single Kotlin codebase. You’ll work at the cutting edge of the KMP ecosystem, shipping production code to real users on multiple platforms, while keeping platform-specific layers feeling truly native. We’re looking for a Kotlin expert who has been in the trenches with KMP — someone who has wrestled with interop edge cases, navigated the wasm target, and shipped Compose Multiplatform UI that feels polished everywhere. What You'll Do - Design and build shared KMP modules covering networking, data persistence, business logic, and domain models used across Android, iOS, and wasm targets. - Ship production-grade Compose Multiplatform UIs that feel native and performant on Android, iOS, and web. - Build optimized platform-specific targets — leveraging Swift/Obj-C interop for iOS and Kotlin/Wasm for web — to meet the performance and UX bar of each platform. - Architect clean platform-expect/actual boundaries and maintain Kotlin/Native and Kotlin/JS interop layers. - Set up and manage Koin Multiplatform for dependency injection across all targets, ensuring clean and testable module graphs. - Collaborate with platform teams (iOS, Android, web) to align on shared APIs, versioning, and release cadences. - Drive KMP best practices across the organization — tooling, testing strategies, CI/CD for multiplatform builds. - Lead development of new AI and media-driven features within the shared codebase. - Participate in architecture reviews and uphold high standards for shared module design and testability. Qualifications - 8+ years of software engineering experience with deep, expert-level Kotlin — you know the language spec, not just the idioms. - Real-world, production KMP experience: you’ve shipped KMP code that real users ran on Android, iOS, and/or wasm — not just toy projects or internal tools. - Hands-on Compose Multiplatform experience: building shared UI across multiple targets with platform-specific adaptations. - Experience with Koin Multiplatform for dependency injection across KMP targets in production codebases. - Solid understanding of Kotlin/Native memory model, freezing, and interop with Obj-C/Swift APIs. - Experience targeting Kotlin/Wasm and/or Kotlin/JS, including the Compose for Web stack. - Fluency with multiplatform build tooling: Gradle multiplatform plugin, source sets, target configuration, and CI pipelines. - Strong grasp of coroutines, Flow, and structured concurrency — understanding how they behave across targets. - Ability to write readable, maintainable, thoroughly documented, and well-tested shared code. - Bonus: experience with KMP libraries (Ktor, SQLDelight) in production environments. - Bonus: contributions to the KMP/CMP open-source ecosystem or close familiarity with JetBrains’ roadmap. Compensation The anticipated annual base salary range for this role is between $180,000-$230,000. When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data. Benefits - Competitive salary and generous company equity - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support - 401(k) retirement savings plan - Lifestyle spending account – $500/month to use however you’d like - Complimentary lunch and snacks for in-office employees - One Medical membership, and more!

Role Description As an Android Engineer at Cantina, you’ll lead the development of innovative, high-performance features — from crafting personalized feeds and immersive custom UIs to empowering bots with AI-driven capabilities like chatting, text-to-speech, voice selection, image creation, and real-time media effects. Working closely with Product Managers, Designers, and Sean Parker, you’ll influence the direction of a product central to the company’s mission, move quickly to deliver impactful updates, and enjoy the autonomy to bring your ideas to life. We’re looking for someone who thrives on pushing the limits of Android development and has a passion for creating visually stunning, high-performance experiences. Your expertise in custom graphics, animation frameworks, and media pipelines will help shape the creative foundation of our flagship app. - Build and ship high-quality Android features using Jetpack Compose, Kotlin Coroutines, and modern Android architecture patterns. - Develop and optimize video/audio pipelines, real-time media effects, and AI-powered visual features on Android. - Craft immersive custom UI experiences with deep expertise in Compose animations, custom drawing, and motion design. - Leverage deep Android internals knowledge — memory management, rendering pipeline, Choreographer, RenderThread — to deliver buttery-smooth performance. - Architect and maintain clean MVVM application structure with well-separated concerns across UI, domain, and data layers. - Set up and manage dependency injection using Koin or Hilt, ensuring scalable and testable module graphs. - Participate in architecture discussions and uphold high standards for code quality, testability, and maintainability. - Collaborate with Product, Design, and QA teams to deliver polished, scalable experiences. - Take ownership of features as part of a small, fast-moving team shipping to a large user base. Qualifications - 8+ years of experience developing Android applications, or equivalent. - Expert-level Kotlin — idiomatic usage, coroutines, Flow, and advanced language features. - Deep Jetpack Compose expertise: custom layouts, state management, animation APIs (Animatable, Transition, AnimationSpec), and performance profiling. - Strong command of MVVM architecture and the Android architecture components ecosystem (ViewModel, LiveData/StateFlow, Room, Navigation, WorkManager). - Hands-on experience with dependency injection using Koin or Hilt in production Android apps. - Strong understanding of Android internals: View system, RenderThread, Choreographer, garbage collection, and battery/memory profiling. - Experience with media frameworks: ExoPlayer, MediaCodec, Camera2/CameraX, and real-time effects. - Ability to write readable, maintainable, documented, and well-tested code. - Experience with RESTful API integrations and proficiency with Git. - Keen attention to detail, motion, and animation — you notice when something is 2px off. - Bonus: experience with AI-driven media features, real-time video/audio effects, or WebRTC on Android. Compensation The anticipated annual base salary range for this role is between $180,000-$240,000. When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data. Benefits - Competitive salary and generous company equity. - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina. - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support. - 401(k) retirement savings plan. - Lifestyle spending account – $500/month to use however you’d like. - Complimentary lunch and snacks for in-office employees. - One Medical membership, and more!

• Designing model evaluation pipelines for models in development and production • Designing user studies for subjective model evaluations. • Converting requirements into measurable metrics. • Designing and developing automated evaluation dashboard to see model performances and compare results. • Training new models to capture new and different evaluation metrics. • Communicating with the model team to help design better models based on the evaluation results. • Communicating with the data team to help decide the type of data necessary to improve model performance. • Communication with the product-manager to make sure product requirements are correctly measured. • Help grow the evaluation team as the founding member. • Lead the evaluation team in the future.

• Dataset ownership: define specs; audit and curate large-scale audio/text; close corpus gaps and fix sample-level issues. • Quality instrumentation: build automated gates/metrics (e.g., SNR, clipping, VAD, WER, SV/LID, safety) with dashboards; validate against listening tests. • Classifiers and filters: train lightweight models to tag, score, and filter data (VAD, ASR gating, LID, SV/diarization, noise/safety); calibrate to subjective outcomes. • Cleaning and integrity: apply denoise/dereverb/de-clip when beneficial; deduplicate and decontaminate; prevent leakage; maintain lineage and versioned releases. • Data selection: optimize mixtures via sampling, weighting, curriculum, and active learning; mine hard negatives and long-tail cases. • Tooling and pipelines: ship reproducible ETL and validation; integrate quality gates into training/eval; add monitoring and alerts. • Human-in-the-loop and compliance: run MTurk/vendor annotation with strong QC; ensure consent/licensing/policy compliance; collaborate across teams and document datasets.

• Designing model evaluation pipelines for models in development and production • Designing user studies for subjective model evaluations. • Converting requirements into measurable metrics. • Designing and developing automated evaluation dashboard to see model performances and compare results. • Training new models to capture new and different evaluation metrics. • Communicating with the model team to help design better models based on the evaluation results. • Communicating with the data team to help decide the type of data necessary to improve model performance. • Communication with the product-manager to make sure product requirements are correctly measured. • Help grow the evaluation team as the founding member. • Lead the evaluation team in the future.