Software Engineer, Model Serving Infrastructure
The role involves contributing to the development of next-generation, high-performance machine learning serving systems. Responsibilities include building infrastructure that powers AI applications, working on problems at the intersection of distributed systems, machine learning, and high-performance computing, and solving fundamental computer science problems impacting AI deployment. Specific projects include implementing asynchronous inference for non-blocking client requests, designing intelligent request routing systems to balance load across thousands of model replicas with strict latency SLAs, building traffic management systems for zero-downtime model updates handling terabytes of inference requests, improving state management for scale from thousands to tens of thousands of replicas, architecting frameworks for multi-model orchestration in complex ML pipelines ensuring end-to-end latency guarantees, and developing observability and debugging tools for distributed ML applications at scale. The work involves writing performance-critical code in Python (with Cython optimizations) and potentially C++, working with distributed systems at scale using Ray Core's actor system, gRPC, and custom networking protocols, extending cloud-native infrastructure such as Kubernetes and service meshes, gaining system-level knowledge of ML/AI frameworks like TensorFlow, PyTorch, JAX, and transformers, and ensuring production reliability with tools like OpenTelemetry, Prometheus, distributed tracing, and chaos engineering to maintain 99.99% uptime. The role also involves leveraging AI coding agents to enhance team productivity while maintaining high code quality standards.
Engineering Manager, Cooperative Systems
Lead and grow a small team building applied AI systems for internal operations. Design and build AI-powered automation systems in close proximity to customers. Stay hands-on in architecture and implementation across the full stack. Develop evolving systems spanning developer tools, automation platforms, knowledge graphs, and data systems. Deploy systems directly to internal users and close customers to iterate rapidly based on real-world feedback. Engage frequently with scaled workforces to understand needs and validate solutions. Create systems for visibility and learning in hybrid workforces. Partner with product, research, and ops teams daily.
AI/ML Engineer
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
Software Engineer, Early Career
As a Software Engineer at Mirage, you will work across product engineering, backend/platform engineering, and applied AI teams. Responsibilities include designing and building systems, APIs, and infrastructure that power products; solving challenges involving distributed systems, scaling, and performance; integrating and operating large AI models in production; building core platform components such as storage, billing, observability, and security; shipping end-to-end product experiences for creative workflows; building polished, performant user interfaces (web or native mobile); pushing the boundaries of video, graphics, and AI-powered creation tools; instrumenting, A/B testing, and iterating quickly with real user data; building and shipping AI-powered product experiences end-to-end; working with state-of-the-art models across video, audio, image, and text; designing systems for context, reasoning, and intelligent behavior; and building evals, datasets, and tooling for improving model quality.
Staff Software Engineer, AI Platform
Design and build abstractions and platform-level systems that improve all of Harvey's agentic products. Own infrastructure for model integration, routing, and evaluation that helps Harvey choose and deploy the right foundation model for any given context. Build evaluation frameworks and tooling that let every team across Harvey iterate on AI quality effectively. Partner closely with product engineering teams, PMs, and design to launch cutting-edge AI products. Evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge.
Machine Learning Research, RF Foundation Models Specialist
Formulate new machine learning problems in RF sensing and spectrum understanding. Design experiments and evaluation approaches reflecting real operating conditions such as domain shift, changing interference, and varying sensors and platforms. Build models for structured, noisy, and partially observed signal environments. Improve robustness across propagation, interference, and low-visibility waveform conditions. Optimize models for throughput, latency, and deployment constraints. Move promising research into a release path for real systems through proofs-of-concept, realistic validation, and conversion into maintainable, deployable code. Use field performance to inform the development of the next generation of models and tooling. Work across the lifecycle of research and deployment including data and evaluation design, experimentation, model development, release readiness, and iteration based on real-world outcomes. Collaborate closely with embedded, hardware, and mission teammates, influencing how machine learning capability is built as the company scales.
Software Engineer - Voice AI (Inference Runtime)
Own and lead Baseten Voice AI product areas end-to-end, including architecture, system design, implementation, rollout, and long-term production operations. Design, build, and operate real-time, large-scale, high-performance model-serving systems for STT, TTS, and voice agent workloads with clear service level objectives for mission-critical customer deployments. Drive cross-team collaboration with sister engineering teams to address full-stack technical problems, align priorities, and coordinate end-to-end delivery across the product surface. Mentor teammates through code reviews, design documentation, and provide technical leadership.
Machine Learning Engineer
As a Data Scientist (Algorithm Engineer) in Delivery, you will work closely with Simulation Engineers, Machine Learning Engineers, and customers to understand and define engineering and physics challenges, while providing technical leadership to your team. Your responsibilities include leading the pre-processing and analysis of complex data to prepare it for predictive modelling, establishing best practices and methodologies for your team, architecting and developing innovative deep learning models combined with optimisation methods to predict and control physical systems, and taking full responsibility for the quality, accuracy, and impact of your work and your team's work. You will design, build, and test data pipelines that are reliable, scalable, and easily deployable in production environments, lead cross-functional collaboration to ensure model integration with simulations, drive internal research and product development, mentor junior team members, lead communication and presentations with technical teams and customers, and represent the company as a technical authority when visiting customer sites globally. Additionally, as a senior member, you will influence technical direction and shape future solutions and products while developing leadership skills.
Computer Vision Engineer
The Computer Vision Engineer will deliver hands-on computer vision work and architect technical solutions for complex project requirements. They will lead the technical delivery of computer vision projects and provide expert guidance to multidisciplinary teams throughout the development lifecycle. The role includes contributing expert computer vision insight to bids and identifying opportunities to integrate advanced visual intelligence into customer solutions. The engineer will stay at the forefront of the field by mastering State-of-the-Art developments and sharing best practices across the business unit. They will represent the organization internally and externally as a subject matter expert in computer vision, partner with leadership to define the technical strategy for computer vision work, take ownership of capability development within the Defence domain, and mentor and develop team members interested in computer vision, fostering a continuous learning and technical excellence environment.
Proposal and Capture Manager
You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This involves understanding the AI systems built at Helsing, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.
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