Creative Technologist, HCI
You will explore and build experimental AI experiences that help define how people interact with proactive AI systems, working at the intersection of design, engineering, and AI experimentation. Responsibilities include prototyping experimental AI interfaces beyond standard chat UI, building quick proof-of-concept experiences using AI models, APIs, and frontend tools, exploring new ways for users to understand, direct, and collaborate with AI systems, testing interaction ideas around human control, trust, transparency, memory, multimodal input, and long-running workflows, working with product, design, and ML teams to turn early ideas into testable prototypes quickly, identifying useful HCI patterns from research, products, and emerging tools to test their applicability for A1, documenting experiments clearly by detailing what was tested, what worked, what failed, and suggesting what should be built next, and contributing to the team's understanding of what HCI should look and feel like in a real consumer app.
Technical Product Manager, AI Systems
Work directly with engineers on system design, evaluation, and trade-offs while defining requirements and shaping how the AI system works for global users. Research and define end-to-end AI system requirements from capability to behavior to user impact. Translate model capabilities, data constraints, and evaluation results into clear product and system decisions. Make trade-offs across quality, latency, cost, reliability, and user experience. Collaborate closely with ML, backend, and mobile engineers on system design, evaluation, and iteration. Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback. Drive execution with clear specifications, strong judgment, and disciplined prioritization. Ensure systems ship quickly, safely, and reliably with strong feedback loops. Own product quality end-to-end, ensuring correctness, predictability, and user trust.
Staff Machine Learning Engineer
Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems managing 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 into backend, mobile, and desktop products. Make pragmatic trade-offs, ship improvements quickly, and learn from real usage. Work under real production constraints including latency, cost, reliability, and safety. Detect, debug, and resolve production issues quickly to minimize user impact. Support and align team members to deliver high-impact ML work with minimal friction. Ensure iterations on models and systems are measurable, safe, and improve user experience over time.
Technical Lead, Machine Learning
Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, 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 into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly while learning from real usage. Work within real production constraints such as latency, cost, reliability, and safety.
Android Software Engineer
As an Android Software Engineer, you own the Android client experience, how AI feels, behaves, and performs on mobile devices. You will build and maintain production Android apps using Kotlin where AI interactions are core to the product. Responsibilities include integrating AI-powered features via backend APIs, designing UX patterns for AI interactions such as streaming responses, retries, and partial results, optimizing performance, memory usage, and responsiveness for AI-heavy flows, implementing analytics, logging, and feedback capture to support AI evaluation and iteration, collaborating closely with backend and ML engineers on API contracts and system behavior, and ensuring app stability, security, and scalability in production environments.
Senior Machine Learning Engineer
As a Senior Member of Technical Staff, Machine Learning, you are responsible for building core ML systems that power a proactive, long-horizon AI product and owning work end-to-end including data preparation, training, evaluation, inference, and iteration. You turn research ideas into working systems that run reliably in production, debug model failures and system issues using real production signals, iterate quickly by shipping, measuring outcomes, refining, and repeating. You collaborate closely with research, product, and engineering teams to deliver real user impact, mentor and review work from other ML engineers through example and technical judgment, and work under real production constraints like latency, cost, reliability, and safety.
Member of Technical Staff, Machine Learning
As a Member of Technical Staff, Machine Learning, the responsibilities include building and improving ML components across data, training, evaluation, and inference; fine-tuning and adapting models as part of larger production systems; implementing evaluation and testing to understand model behavior; helping build and maintain data pipelines for real-world and synthetic data; debugging model issues, performance problems, and production incidents; shipping improvements iteratively and learning from real user feedback; working closely with senior ML engineers and product teams; and working under real production constraints such as latency, cost, reliability, and safety.
Full Stack Engineer, AI systems
Build end-to-end product features across frontend, backend, and AI integrations; design agent workflows that handle planning, tool use, failure, and recovery across multiple steps; integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions; design real-time AI interactions with streaming, partial results, and tight latency constraints; improve system reliability, observability, and fallback mechanisms; collaborate closely with ML, backend, and product teams to ship features end-to-end; continuously iterate based on real usage and failure modes.
Backend Engineer, AI (Agent Systems)
As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. You build and operate backend systems that serve AI-powered features in production, design inference pipelines, orchestration layers, and service boundaries around models. You are responsible for production concerns such as monitoring, logging, alerting, and incident response. Additionally, you optimize latency and throughput across inference, caching, batching, and streaming. Your work enables backend systems to run reliably at scale, handling production AI traffic with low latency and high throughput, ensuring APIs are stable, clear, and support seamless integration with frontend and ML systems. You ensure production incidents are quickly detected, diagnosed, and resolved, minimizing user impact, and continuously improve system performance and reliability through iterative changes based on real usage.
Applied AI Engineer
As an Applied AI Engineer, you will turn model capabilities into real product behavior by owning problems end-to-end, from shaping model behavior to building the systems around it and ensuring reliable performance in production. Responsibilities include building and shipping AI features end-to-end, designing and iterating on prompts, tools, memory, and agent workflows, turning raw model outputs into structured and predictable behaviors, debugging issues across the full stack including model, orchestration, infrastructure, and user experience, optimizing for latency, cost, and production reliability, developing lightweight evaluation frameworks to measure real-world performance, and working closely with product and engineering teams to translate ambiguous problems into working systems.
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