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.
Partner AI Deployment Engineer - AWS
The Partner AI Deployment Engineer for AWS serves as the senior technical counterpart to AWS field leadership, building trust and credibility while influencing joint account strategy and technical direction for high-priority opportunities. The role involves shaping engagement models, defining prioritization frameworks, and establishing best practices for AWS collaborations. Responsibilities include leading technical strategy for large, complex enterprise engagements from ideation through architecture design, prototyping, and production deployment, acting as a technical decision-maker and escalation point to de-risk implementations. The engineer designs and communicates end-to-end AI architectures using OpenAI and AWS services, builds and guides development of prototypes and reference implementations, and ensures solutions are scalable, secure, and production-ready. They enable AWS and partners via scalable technical motions such as workshops, playbooks, and demos, develop reusable solution assets deployable independently by AWS teams, mentor partner technical teams to achieve self-sufficiency, and extend impact through GSIs, RSIs, and ISVs. Additionally, the role partners with various internal functions to align strategy and execution, acts as a bridge between field and product teams delivering insights to inform roadmaps, and contributes to internal knowledge systems defining standards and playbooks for the AI Deployment Engineering function.
Staff Engineer, Software Autonomy Applications (R4987)
In this role, you will work closely with customers to understand their requirements, provide technical expertise and customer support during deployment, and ensure successful integration of Hivemind. You will deploy with customers on site, approximately 50% travel, to support software integration and development activities. Become an expert user of the Hivemind enterprise software stack and its various autonomy modules. Provide technical support and training to customers on use of Hivemind. Develop AI & Autonomy applications using the Shield AI enterprise software development kit. Assist the sales team in pre-sales activities such as demos, conferences, and immersions. Assist in post-sales deployment and integration of Shield AI enterprise software products. Develop and maintain technical documentation and training materials. Help customers debug software/API integration issues. Collaborate with the product engineering team to address customer feedback and improve products.
Manager, AI Deployment Engineering (Korea)
The AI Deployment Engineering Manager in Korea is responsible for owning the strategy and operating model of the AI Deployment Engineering team to ensure alignment with company objectives and customer needs. They lead, build, and mentor a team of AI Deployment Engineers to deliver exceptional customer results, as measured by production customer applications and API adoption. The role involves serving as the technical advocate for customers by synthesizing their needs to inform research and product/engineering roadmaps, acting as the primary technical escalation point during development, maintaining direct communication with executive-level stakeholders, and serving as an industry thought leader to champion the safe and innovative use of the technology across sectors.
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