About the RoleA1 is building a proactive AI system that users rely on daily across conversations, tools, and workflows.You will build and maintain production Android applications where AI is a core part of the user experience. This role focuses on performance, reliability, and thoughtful UX for AI-heavy interactions, not thin-client glue code.
FocusBuild and maintain production Android apps using Kotlin.Integrate AI-powered features (chat, vision, voice, recommendations) via backend APIs.Design UX patterns for AI interactions, including streaming responses, retries, and partial results.Optimize performance, memory usage, and responsiveness for AI-heavy flows.Implement analytics, logging, and feedback capture to support AI evaluation and iteration.Collaborate closely with backend and ML engineers on API contracts and system behavior.Ensure app stability, security, and scalability in production environments.
Ideal Experiences3+ years of Android development experience using Kotlin.Hands-on experience integrating AI features (e.g. LLM, vision, speech APIs).Strong understanding of asynchronous programming (Coroutines, Flow).Familiarity with REST or gRPC APIs and structured data formats.Strong debugging and performance profiling skills.Comfort building in environments with latency, partial failure, and non-deterministic behavior.Experience with MLKit or light on-device inference.Published production apps on the Google Play Store.
Tech StackKotlin / JavaSQL / noSQLTensorFlow Lite (on-device inference)
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system used daily across conversations, tools, and workflows.You will own how this system behaves on desktop environments. Your work focuses on reliability, performance, and real-time behavior in production desktop applications.
FocusBuild and maintain cross-platform desktop applications using Electron.Design responsive and scalable UIs for real-time collaboration.Implement desktop-specific functionality including file system access, native notifications, auto-updates, and deep linking.Integrate AI-powered features (chat, agents, AI assistance) via backend APIs.Optimize startup time, memory usage, and runtime performance.Profile and reduce Electron overhead.Manage large local state and message history efficiently.Ensure smooth real-time updates (messages, typing indicators, presence).Maintain stability across macOS and Windows environments.
Ideal ExperiencesProven software engineering experience.Hands-on experience building production Electron applications.Strong proficiency in JavaScript and TypeScript.Experience with React or similar UI frameworks.Solid understanding of the desktop application lifecycle.Experience with IPC communication.Experience working with local storage (SQLite, IndexedDB, filesystem).Experience with WebSockets or other real-time transport mechanisms.Strong debugging and performance profiling skills.Familiarity with native OS behaviors on macOS or Windows.
Tech StackElectronNode.jsTypescriptSQl & noSQL
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time.You will be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of A1’s intelligence – training pipelines, inference systems, evaluation tooling, and deployment.
What You'll DoBuild and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.Architect and operate scalable inference systems, balancing latency, cost, and reliability.Design and maintain data systems for high-quality synthetic and real-world training data.Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.Make pragmatic trade-offs and ship improvements quickly, learning from real usage.Work under real production constraints: latency, cost, reliability, and safety
Tech StackPythonPyTorch / JAXGPU-based training and inference system
Ideal ExperienceYou have built or shipped real ML systems used by people, not just demos.You are comfortable working with large models and understanding their failure modes.You write strong, production-grade code and care about system correctness.You are self-directed, pragmatic, and take full ownership of outcomes.You communicate clearly and collaborate well in small, high-trust teams.
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time.You will be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of A1’s intelligence – training pipelines, inference systems, evaluation tooling, and deployment.
FocusBuild and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.Architect and operate scalable inference systems, balancing latency, cost, and reliability.Design and maintain data systems for high-quality synthetic and real-world training data.Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.Make pragmatic trade-offs and ship improvements quickly, learning from real usage.Work under real production constraints: latency, cost, reliability, and safety
RequirementsStrong background in deep learning and transformer-based architectures.Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).Strong software engineering fundamentals – you write robust, maintainable, production-grade systems.Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.Comfort owning ambiguous, zero-to-one ML systems end-to-end.A bias toward shipping, learning fast, and improving systems through iteration.
Ideal ExperienceExperience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.Contributions to open-source ML or systems libraries.Background in scientific computing, compilers, or GPU kernels.Experience with RLHF pipelines (PPO, DPO, ORPO).Experience training or deploying multimodal or diffusion models.Experience with large-scale data processing (Apache Arrow, Spark, Ray).
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that carries work forward across conversations, tools, and time.You will build the systems that power every AI interaction users experience. Your work will sit at the intersection of models, orchestration, APIs, and product behavior - where correctness, latency, and reliability actually matter.
FocusBuild and operate backend systems that serve AI-powered features in production.Design inference pipelines, orchestration layers, and service boundaries around models.Own production concerns: monitoring, logging, alerting, and incident response.Optimize latency and throughput across inference, caching, batching, and streaming.
Ideal ExperiencesStrong backend engineering fundamentals in production environments.Experience running high-throughput, low-latency services.Familiarity with AI inference patterns (LLMs, embeddings, multimodal).Comfortable debugging distributed systems under load.Bias toward shipping and learning from production behavior.
Tech StackPythonNodeJsPytorchOpenAI / Anthropic / open-source LLMsSQl & noSQLKubernetesDocker
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that carries work forward across conversations, tools, and time.As VP of Engineering - Applications, you will turn intelligence into a real product. You are responsible for the engineering systems, teams, and execution that turn ML capability into reliable, fast, and intuitive user experiences across backend, mobile, and desktop platforms.This role is a senior engineering leader who stays close to the product, makes hard technical decisions, and holds a high bar for quality and delivery.
What You'll be DoingOwn 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.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.
What You Will NeedSignificant 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.Low ego, strong judgment, and the ability to raise the technical bar for the entire applications organization.
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time.You will work on core ML systems under the guidance of senior engineers. This role is for builders who want to grow their judgment by shipping real ML systems in production.
FocusBuild and improve ML components across data, training, evaluation, and inference.Fine-tune and adapt models as part of larger production systems.Implement evaluation and testing to understand model behavior.Help build and maintain data pipelines for real-world and synthetic data.Debug model issues, performance problems, and production incidents.Ship improvements iteratively and learn from real user feedback.Work closely with senior ML engineers and product teams.Work under real production constraints: latency, cost, reliability, and safety
Tech StackPythonPyTorch / JAXProduction ML systems running on GPUs
Ideal ExperienceStrong foundations in machine learning and modern neural architectures.Some hands-on experience training, fine-tuning, or deploying ML models.Comfortable writing production-quality code and learning new tools quickly.Curious, coachable, and eager to learn from real systems in production.Able to work through ambiguity with guidance and grow ownership over time.Bias toward shipping, iteration, and continuous improvement.
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that carries work forward across conversations, tools, and time.You will be a core owner of significant ML systems in production. This role is for engineers who can independently drive complex ML problems from idea to deployment and make strong trade-offs along the way.
FocusBuild core ML systems that power a proactive, long-horizon AI product.Own work end-to-end: data preparation, training, evaluation, inference, and iteration.Turn research ideas into working systems that run reliably in production.Debug model failures and system issues using real production signals.Iterate quickly: ship, measure outcomes, refine, and repeat.Collaborate closely with research, product, and engineering to deliver real user impact.Mentor and review work from other ML engineers through example and technical judgment.Work under real production constraints: latency, cost, reliability, and safety
Tech StackPythonPyTorch / JAXGPU-based training and inference systems
Ideal ExperienceYou have built and shipped ML systems used by real users.You understand how modern ML models behave — and misbehave — in production.You write strong, production-quality code and think in systems, not scripts.You take ownership, work independently, and push work across the finish line.You learn fast, communicate clearly, and improve through iteration.
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time.You will be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of A1’s intelligence – training pipelines, inference systems, evaluation tooling, and deployment.
What You'll DoBuild and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.Architect and operate scalable inference systems, balancing latency, cost, and reliability.Design and maintain data systems for high-quality synthetic and real-world training data.Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.Make pragmatic trade-offs and ship improvements quickly, learning from real usage.Work under real production constraints: latency, cost, reliability, and safety
Tech StackPythonPyTorch / JAXGPU-based training and inference system
Ideal ExperienceYou have built or shipped real ML systems used by people, not just demos.You are comfortable working with large models and understanding their failure modes.You write strong, production-grade code and care about system correctness.You are self-directed, pragmatic, and take full ownership of outcomes.You communicate clearly and collaborate well in small, high-trust teams.
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
About the RoleA1 is building a proactive AI system that users rely on daily across conversations, tools, and workflows.You will define how AI feels in users’ hands. This role focuses on building production-quality iOS applications where AI is a core interaction layer, not an add-on. Your work will directly impact usability, trust, and perceived intelligence of the product.
FocusBuild and maintain iOS applications using Swift and SwiftUI.Integrate AI-powered features through backend APIs.Design UX patterns for AI interactions, including loading states, streaming responses, retries, and fallbacks.Optimize performance, memory usage, and battery efficiency.Capture user signals and feedback to support AI evaluation and iteration.Collaborate closely with backend and ML engineers on API design and system behavior.Maintain high App Store quality, stability, and production reliability.
Ideal Experiences3+ years of iOS development experience using Swift.Hands-on experience integrating AI-powered features into mobile apps.Strong understanding of async/await, concurrency, and background tasks.Solid iOS performance and memory optimization skills.Experience shipping and maintaining production iOS apps.Comfort designing for latency, partial failure, and non-deterministic AI behavior.Exposure to CoreML or light on-device ML.Familiarity with feature flags or remote configuration systems.Strong intuition for AI UX patterns and user trust considerations.
Tech StackSwiftSPMSwiftUISQL / noSQLTensorFlow Lite (on-device inference)
How We WorkOur organization is very flat and our team is small, highly motivated, and focused on engineering and product excellence. All members are expected to be hands-on and to contribute directly to the company’s mission.
Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
