Forward Deployed Engineer - ML
As a Forward Deployed ML Engineer, you will work hands-on with companies such as Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal's platform. You will contribute to open-source projects like SGLang and publish technical content showcasing Modal's capabilities across the AI stack. Collaboration with Modal's product and sales teams is expected, serving both as an engineer and a product stakeholder. You will build trusted relationships with technical leaders including CTOs, VPs of Engineering, and ML leads at frontier AI companies. Additionally, you will conduct technical demos, experiments, and proof-of-concepts to demonstrate Modal's performance advantages.
Global Hardware Sourcing & Supply Manager
The responsibilities for the Global Hardware Sourcing & Supply Manager role include advancing inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. The role involves implementing and maintaining changes in high-performance inference engines, profiling and optimizing performance across GPU, networking, and memory layers to improve latency, throughput, and cost. It also requires unifying inference with RL/post-training by designing and operating RL and post-training pipelines and making these workloads more efficient with inference-aware training loops. The role includes training, evaluating, and iterating on frontier models using these pipelines, co-designing algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, and quickly identifying bottlenecks across various components. Running ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, and owning critical production-scale systems by profiling, debugging, optimizing inference and post-training services are also key responsibilities. The role involves driving roadmap items that require engine modifications, establishing metrics, benchmarks, and experimentation frameworks, and providing technical leadership by setting technical direction for cross-team efforts and mentoring engineers and researchers on full-stack ML systems and performance engineering.
Machine Learning Engineer
As a Machine Learning Engineer at Faculty, you will deliver bespoke, impactful AI solutions for diverse clients by building and deploying production-grade machine learning software, tools, and infrastructure. You will create reusable, scalable solutions that accelerate the delivery of ML systems. Your work includes 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 standards for deploying machine learning at scale, and acting as a technical advisor to customers and partners by translating complex ML concepts for stakeholders.
Senior MLOps Engineer
As a Senior MLOps Engineer, you will lead development and deployment of cutting-edge AI systems, designing, building, and deploying scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards. You will be responsible for leading technical scoping and architectural decisions for high-impact ML systems, defining and implementing best practices and standards for deploying machine learning at scale across the business. Collaboration with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities is key. You will act as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies. Additionally, mentoring and developing junior engineers and actively shaping the team's engineering culture and technical depth are part of your duties.
MLOps Engineer
The MLOps Engineer is responsible for building and deploying production-grade machine learning software, tools, and infrastructure, creating reusable and 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 standards for deploying machine learning at scale, and acting as a technical advisor to customers and partners by translating complex ML concepts for stakeholders.
Senior ML Operations (MLOps) Engineer
The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.
Manual Quality Assurance Engineer, Web Core Product - Lviv, Ukraine
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 then design and implement solutions to address the highest priority issues.
Member of Technical Staff - ML Engineering
Deploy, maintain, and optimize production and research compute clusters. Design and implement scalable and efficient ML inference solutions. Develop dynamic / heterogeneous compute solutions for balancing research and production needs. Contribute to productizing model APIs for external use. Develop infrastructure observability and monitoring solutions.
Machine Learning Engineer (Foundation Models & Personalization)
The Machine Learning Engineer is responsible for building and deploying machine learning models that enhance sleep experiences through personalization, prediction, and behavior understanding, including readiness forecasting, event detection, and individualized recommendations. They will apply and adapt foundation-model capabilities to product workflows, develop user behavior models connecting longitudinal signals to actionable interventions, and design evaluation strategies for offline metrics, slice-based analysis, calibration, reliability, and fairness. The role involves partnering with Product teams to run high-quality online experiments, productionizing models via scalable training and inference pipelines, model monitoring, drift detection, alerting, and continuous improvement loops. Collaboration with cross-functional partners such as Product, Mobile, Backend, and Clinical teams is essential to scope requirements and deliver high-impact features.
Senior Machine Learning Engineer - Australia
As a Senior Machine Learning Engineer at Neara, you will create machine learning models that drive the digitisation of real-world infrastructure from various data sources such as LIDAR, imagery, and vector data. You will work at every stage of the ML lifecycle, including data collection, quality assurance, training, and model monitoring. You will decide which problems are suitable for machine learning solutions, define the ML strategy, and stay updated with best practices in data handling, MLOps, and the latest advancements in machine learning to integrate new techniques into the platform. Responsibilities also include developing approaches to generate accurate electric networks from imperfect data using deep learning and classical ML algorithms, developing and optimizing training pipelines, scaling model serving for different problems, improving model QA speed and identifying data and distribution drift, working with diverse data sources and building scalable data pipelines for training and serving, and mentoring junior engineers in best practices for model training and software engineering.
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