Staff Strategic Sourcing Manager (Hardware)
Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines including kernel backends, speculative decoding, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate reinforcement learning (RL) and post-training pipelines to jointly optimize algorithms and systems where most of the cost is inference. Make RL and post-training workloads more efficient with inference-aware training loops such as asynchronous RL rollouts and speculative decoding. Use these pipelines to train, evaluate, and iterate on frontier models on top of the inference stack. Co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, identifying bottlenecks across the training engine, inference engine, data pipeline, and user-facing layers. Run ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, and feed insights back into model, RL, and system design. Own critical systems at production scale by profiling, debugging, and optimizing inference and post-training services under real production workloads. Drive roadmap items requiring engine modification including changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks for rigorous validation of improvements. Provide technical leadership by setting technical direction for cross-team efforts, and mentor engineers and researchers on full-stack ML systems work and performance engineering.
Tech Lead, Android Core Product - Nairobi, Kenya
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for various use cases; deploy and operate core ML inference workloads for the AI Voices serving pipeline; introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models; build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Tech Lead, Android Core Product - Islamabad, Pakistan
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Tech Lead, Android Core Product - Karachi, Pakistan
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions to address the highest priority issues.
Tech Lead, Android Core Product - Leipzig, Germany
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for various use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to monitor bottlenecks and instability sources, then design and implement solutions to address the highest priority issues.
Staff Machine Learning Engineer
Define Adaptive's ML strategy including where ML should be applied across products, required infrastructure, and build vs. buy decisions. Design and build production ML systems end-to-end including data pipelines, model training, evaluation frameworks, and inference serving. Establish evaluation methodology to measure model quality, catch regressions, and make data-driven decisions about model changes. Own the strategy for acquiring and formatting necessary data, including labeling, feedback loops, and model improvement over time. Partner with product engineers to integrate ML into the product by writing production code and working within existing codebase. Help build and lead the ML team as scope grows.
Machine Learning Engineer, Distributed Data Systems
Design, build, and maintain data infrastructure systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure, and machine learning infrastructure while ensuring scalability, reliability, and security. Ensure the data platform can scale by orders of magnitude while remaining reliable and efficient. Partner with researchers to deeply understand requirements and translate them into production-ready systems. Harden, optimize, and maintain critical data infrastructure systems that power multimodal training and evaluation.
AI/ML 2026 Internship
As an AI/ML Engineer Intern at Brain Co., you will assist in designing and deploying large language model (LLM)-powered applications to automate complex, real-world workflows. You will build and improve data pipelines and support model training, evaluation, and optimization. Your work involves handling structured and unstructured data, such as text, documents, and logs. You will also help prepare models and systems for production deployment and monitoring. Collaboration with senior engineers, AI researchers, and product teams is expected, along with learning best practices through code reviews, design discussions, and hands-on mentorship. Additionally, you will gain exposure to customer-facing and real-world constraints, including working with public-sector institutions.
Principal Machine Learning Engineer
The role involves building a platform used by Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. The candidate will work on a focused, stream-aligned, and cross-functional team that includes back-end, front-end, and design members, empowered to make its own implementation decisions towards meeting its objectives. Responsibilities include gathering and leveraging domain knowledge and experience from the Data Scientists and Simulation Engineers using the product, taking ownership of work from implementation to production, ensuring quality, scalability, and observability at every step, which includes testing, containerization, continuous integration and delivery, authentication, authorization, telemetry, observability, and monitoring.
Software Engineer, macOS Core Product - Minneapolis-St. Paul, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases; deploy and operate the core ML inference workloads for the AI Voices serving pipeline; introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models; build tools to provide visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
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