Cantina

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.

• Build and maintain data pipelines for large video generation models, including data ingestion, parsing, filtering, preprocessing, and dataset curation at scale, using tools such as AWS S3 and DynamoDB. • Design and run annotation workflows across platforms such as MTurk, Prolific, and Mechanical Turk, including task design, quality control, and label validation. • Train, evaluate, and improve smaller supporting models used for data filtering, quality assessment, preprocessing, or other parts of the ML pipeline. • Partner closely with research and engineering teams to turn experimental workflows into scalable, repeatable systems that support model training and evaluation. • Own data quality across the pipeline by identifying bottlenecks, failure modes, and low-quality sources, and continuously improving tooling and processes. • Build internal tools and automation that make it easier to prepare datasets, launch annotation jobs, monitor outputs, and support model development end to end. • Drive larger pipeline projects from start to finish, such as new dataset creation efforts or upgrades to labeling and preprocessing infrastructure. • Work within a Kubernetes-based training infrastructure, ensuring datasets are properly prepared, formatted, and delivered to training clusters. • Profile and optimize research model inference scripts used in preprocessing steps, ensuring that model-driven filtering and transformation stages run within practical time and cost constraints when applied to large-scale raw data.

About Cantina Cantina is building an agentic security operating system that spans application security, security operations, and agent security. We believe the next generation of security products should do more than aggregate alerts or automate isolated tasks. They should understand context, reason across systems, help teams investigate what matters, and safely take action. This is still an emerging space. Many of the most important risks, design constraints, and product opportunities haven’t been discovered yet. We need people who can help us build the product while also uncovering the unknown unknowns that come with combining security systems and agentic AI. The Role We’re hiring a security engineer who wants to build products in the AI era. You’ve spent years understanding how security teams actually work—how incidents get triaged, how alerts get tuned, how detection logic gets written and maintained, how appsec findings get prioritized. Now you want to build the product you wish existed. This is not a security review role, and it’s not a generic backend engineering position. We need someone whose core instincts come from security—understanding attacker behavior, operational failure modes, what actually matters when a SOC is under pressure—and who can turn that knowledge into product. The AI and product engineering dimensions are real parts of the job, but they’re the growth opportunity, not the entry requirement. If you have strong systems engineering skills and genuine curiosity about how agents, tools, and orchestration work, you’ll learn the rest here. What You’ll Do - Build product capabilities across application security, security operations, and agent security - Turn real security workflows into product experiences and platform primitives - Design systems that ingest, correlate, triage, and act on security signals - Help define safe patterns for agents, tools, permissions, memory, and execution boundaries - Identify hidden risks and failure modes that only someone with real security experience would see - Partner with product and engineering to make strong tradeoffs between speed, usability, and security - Contribute to evaluation, testing, observability, and guardrails for agentic behavior - Raise the team’s overall understanding of security architecture, operations, and AI risk What You Bring The non-negotiable: - Deep experience in one or more of: security engineering, application security, detection engineering, incident response, security operations, or security platform engineering - Strong hands-on experience building and shipping software—you write code, not just review it - The ability to reason clearly in ambiguous spaces and surface risks early Highly valued but learnable here: - Experience with AI/LLM application architecture, agent frameworks, or orchestration systems - Product judgment—translating messy technical workflows into usable product decisions - Comfort working across technical and non-technical teams Relevant Background You’ve likely worked with systems and workflows like these: - SIEMs: Splunk, Elastic, Microsoft Sentinel, Chronicle, Panther, or similar - EDR/XDR: CrowdStrike Falcon, SentinelOne, Microsoft Defender, or similar - SOAR / Automation: Tines, Torq, Cortex XSOAR, or similar - Appsec tooling: Semgrep, Snyk, CodeQL, Burp Suite, Wiz, or similar We don’t expect experience with every tool above. We want someone who has been close enough to these environments to understand how modern security teams investigate, prioritize, and respond. Technical Environment - TypeScript / Node.js (primary stack—willingness to work in this is required, prior experience is preferred) - API and integration-heavy systems - Backend and distributed systems design - Security data models, workflow design, and systems integration Why This Role Is Different Most security product companies hire engineers and teach them security, or hire security people and limit them to advisory roles. We’re looking for someone who can do both: ship real systems and bring the security depth to see what others will miss. You’ll have real influence over what gets built and how. If you’ve been frustrated by security products that clearly weren’t built by anyone who’s actually worked in security, this is your chance to fix that.

• Build product capabilities across application security, security operations, and agent security • Turn real security workflows into product experiences and platform primitives • Design systems that ingest, correlate, triage, and act on security signals • Help define safe patterns for agents, tools, permissions, memory, and execution boundaries • Identify hidden risks and failure modes that only someone with real security experience would see • Partner with product and engineering to make strong tradeoffs between speed, usability, and security • Contribute to evaluation, testing, observability, and guardrails for agentic behavior • Raise the team’s overall understanding of security architecture, operations, and AI risk

• Build and maintain end-to-end data pipelines for large-scale image and video datasets: collection, filtering, augmentation, conditioning alignment, and efficient storage/sampling. • Implement model architectures (diffusion, autoregressive, flow-based, diffusion transformers, etc.) and maintain high-throughput PyTorch training loops for large-scale image and video diffusion models. • Run and manage large-scale training experiments on multi-GPU and multi-node setups (DDP, FSDP, DeepSpeed). Debug training instabilities, loss spikes, and convergence issues. • Apply quantization, pruning, and knowledge distillation techniques to compress models without sacrificing quality. • Collaborate with researchers and translate state-of-the-art research papers into working implementations in our internal codebase (e.g., new attention mechanisms, sampling schedules, or conditioning methods). • Build and maintain evaluation pipelines of image quality, video consistency, and perceptual metrics. • Set up and maintain human annotation and evaluation pipelines using services like AWS GroundTruth. • Profile and optimize training speed, GPU memory utilization, and iteration time. Implement inference optimizations to reduce latency and compute cost. • Work with acceleration toolchains such as torch.compile, Triton, TensorRT, or ONNX where appropriate

Role Description We are looking for a talented AI Research Engineer to join our computer vision research team. In this role, you will work closely with our research team, implementing, training, and evaluating state-of-the-art image and video generation models. You will own the engineering execution that turns research ideas into working systems: - Building robust data pipelines - Running and stabilizing large-scale training - Implementing models from papers - Optimizing for speed/efficiency - Running rigorous evaluations This is a high-impact implementation and execution role. This role is ideal for engineers who enjoy building reliable ML systems and scaling research ideas into production-quality training pipelines. The ideal candidate is someone who gets deep satisfaction from: - Making complex systems work - Translating research ideas into reliable, scalable code - Debugging training instabilities - Delivering measurable improvements in training stability, model quality, and inference efficiency This is an excellent opportunity to work closely with experienced researchers, gain deep hands-on exposure to cutting-edge model training techniques, latest research methods in diffusion/transformer-based generation, large-scale experimentation, and efficiency innovations, all while contributing directly to production-grade models. Qualifications - 2–5 years of hands-on experience building and training ML systems, with strong ownership of results - Fluency in PyTorch: comfortable reading, writing, and debugging both training and inference code - Experience training or fine-tuning generative models (diffusion models, transformers, VAEs, or similar) from scratch or near-scratch - Solid understanding of distributed training workflows and practical debugging of large training runs - Demonstrated ability to read and implement AI research papers in computer vision - Familiarity with cutting-edge computer vision models and research literature in the image and video domain - Experience building data pipelines for large-scale image or video datasets - Strong debugging skills: comfortable diagnosing both engineering bugs and training failures - Strong engineering mindset: writing clean, reliable, debuggable code; profiling tools; handling numerical issues at scale Requirements - Build and maintain end-to-end data pipelines for large-scale image and video datasets: collection, filtering, augmentation, conditioning alignment, and efficient storage/sampling - Implement model architectures (diffusion, autoregressive, flow-based, diffusion transformers, etc.) and maintain high-throughput PyTorch training loops for large-scale image and video diffusion models - Run and manage large-scale training experiments on multi-GPU and multi-node setups (DDP, FSDP, DeepSpeed) - Debug training instabilities, loss spikes, and convergence issues - Apply quantization, pruning, and knowledge distillation techniques to compress models without sacrificing quality - Collaborate with researchers and translate state-of-the-art research papers into working implementations in our internal codebase (e.g., new attention mechanisms, sampling schedules, or conditioning methods) - Build and maintain evaluation pipelines of image quality, video consistency, and perceptual metrics - Set up and maintain human annotation and evaluation pipelines using services like AWS GroundTruth - Profile and optimize training speed, GPU memory utilization, and iteration time - Implement inference optimizations to reduce latency and compute cost - Work with acceleration toolchains such as torch.compile, Triton, TensorRT, or ONNX where appropriate 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!