Staff Engineer, API Core Platform
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. Unify inference with RL and post-training by designing and operating pipelines such as RLHF, RLAIF, GRPO, DPO-style methods, and reward modeling, optimizing jointly algorithms and systems. Make RL and post-training workloads more efficient with inference-aware training loops, async RL rollouts, speculative decoding, and other techniques for large-scale rollout collection and evaluation. Use these pipelines to train, evaluate, and iterate on frontier models on top of the inference stack. Co-design algorithms and infrastructure so objectives, rollout collection, and evaluation are tightly coupled to efficient inference and identify bottlenecks across 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, such as 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 at the intersection of inference, RL, and post-training, and mentor other engineers and researchers on full-stack ML systems work and performance engineering.
Staff Software Engineer, ML Infrastructure
Design and build distributed training platforms for LLM and multimodal fine-tuning and post-training at scale. Implement and integrate state-of-the-art training algorithms into production pipelines. Own inference architecture and multi-provider routing, including failover and optimization. Research and implement inference optimizations including quantization, speculative decoding, and batching strategies. Lead initiatives to improve latency and cost efficiency across the training and serving stack. Build evaluation and experimentation infrastructure that enables rapid, reliable iteration. Drive technical direction, mentor engineers, and establish best practices for ML infrastructure.
Lead Machine Learning Engineer
The Lead Machine Learning Engineer will set the technical direction for complex ML projects, balancing trade-offs and guiding team priorities. Responsibilities include designing, implementing, and maintaining reliable, scalable ML/software systems and justifying key architectural decisions. The role involves defining project problems, developing roadmaps, overseeing delivery across multiple work-streams in ill-defined, high-risk environments, and driving the development of shared resources and libraries across the organisation. The engineer will guide other engineers in contributing to these resources, lead hiring processes, make informed selection decisions, mentor multiple individuals to foster team growth, and develop and execute recommendations for adopting new technologies and changing working methods. Additionally, acting as a technical expert and coach for customers, accurately estimating large work-streams, and defending rationale to stakeholders is required.
Machine Learning Engineer
The Machine Learning Engineer is responsible for building and deploying production-grade machine learning software, tools, and infrastructure. They create reusable, scalable solutions that accelerate the delivery of ML systems. They collaborate with engineers, data scientists, and commercial leads to solve critical client challenges. They lead technical scoping and architectural decisions to ensure project feasibility and impact. They define and implement Faculty's standards for deploying machine learning at scale. Additionally, they act as technical advisors to customers and partners, translating complex ML concepts for stakeholders.
Summer Internship - Machine Learning Engineer
Assist in designing, training, and evaluating machine learning models for real-world use cases. Help build contextualized models and tools for efficient data ingestion and transformation. Perform exploratory data analysis and stay updated on the latest ML research, techniques, and tools. Work closely with cross-functional teams including Data Scientists, Software Engineers, and Product Managers to define project requirements and deliver solutions. Prepare clear, concise technical documentation and report findings to relevant stakeholders. Contribute to code testing processes to ensure robust, high-quality deliverables.
AI/Machine Learning Engineer Intern
As an AI/Machine Learning Engineering Intern, you will contribute to building intelligent product experiences that help students discover and secure opportunities. Your work will span search, recommendations, matching, and other discovery systems that power job exploration on Handshake. You will partner with senior engineers and data scientists to develop machine learning models that improve user experience, build Agentic pipelines/workflows to improve the Handshake student/employer user experience, contribute to experimentation, model evaluation, and performance monitoring. Additionally, you will participate in technical discussions, brainstorming sessions, and team reviews, and document methodologies and findings to support knowledge sharing and long-term system improvements.
Senior MLOps Engineer
As a Senior MLOps Engineer, the responsibilities include leading technical scoping and architectural decisions for high-impact ML systems, designing, building, and deploying production-grade ML software, tools, and scalable infrastructure, and defining and implementing best practices and standards for deploying machine learning at scale across the business. The role also involves collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities, acting as a trusted technical advisor to customers and partners by translating complex concepts into actionable strategies, and mentoring and developing junior engineers while actively shaping the team's engineering culture and technical depth.
Senior Python Engineer
As a Senior Python Engineer, the role involves leading the development and deployment of advanced AI systems for diverse clients, designing, building, and deploying scalable, production-grade machine learning software and infrastructure that adhere to strict operational and ethical standards. Responsibilities include leading technical scoping and architectural decisions for high-impact machine learning systems, defining and implementing best practices and standards for deploying machine learning at scale, collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges, acting as a trusted technical advisor to clients by translating complex concepts into actionable strategies, and mentoring junior engineers while contributing to the team's engineering culture and technical depth.
MLOps Engineer
Building and deploying production-grade ML software, tools, and infrastructure; creating reusable, scalable solutions to accelerate the delivery of ML systems; collaborating with engineers, data scientists, and commercial leads to solve critical client challenges; leading technical scoping and architectural decisions to ensure project feasibility and impact; defining and implementing Faculty’s standards for deploying machine learning at scale; acting as a technical advisor to customers and partners by translating complex ML concepts for stakeholders.
Senior ML Operations (MLOps) Engineer
As a Senior ML Operations Engineer at Eight Sleep, you will pioneer cutting-edge ML technologies and integrate them into products and processes for health monitoring. You will own the design and operation of robust ML infrastructure by building scalable data, model, and deployment pipelines to ensure reliable model delivery to production. Your role involves partnering cross-functionally with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales across Pods globally. You will optimize ML systems for cost-effectiveness, scalability, and high performance by managing compute, storage, and deployment resources during training and inference. Additionally, you will develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment, and maintain clear and direct communication within a remote work environment.
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