Senior Backend Engineer - Vancouver
Backend engineers at Giga are responsible for building the infrastructure and systems that power the company's AI agents. This includes working across the stack, from data pipelines and analytics to real-time agent systems, and shipping features that have a direct impact on customer operations. Engineers own problems end-to-end, including deciding the right approach, building the solution, and iterating based on learnings. Specific projects include migrating the analytics service to ClickHouse to scale growth, improving hallucination detection systems to reduce error rates, and scaling, stabilizing, and enhancing the browser use agent in absence of formal customer APIs. Senior engineers are expected to make sound technical decisions, ship reliably, maintain high code quality and system design standards, and have significant autonomy and influence over product development.
Systems Engineer - Ground and Control (Air)
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 will involve understanding the AI systems built, 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.
Software Engineer, AI Compiler
Work across the full stack with software, systems, and hardware teams to ensure correctness, performance, and deployment readiness for real workloads. Contribute to shaping the long-term compiler architecture and tooling strategy. Design and implement parts of the compiler stack targeting the novel AI accelerator, including front-end lowering, IR transformations, optimization passes, and backend code generation. Build and evolve MLIR/LLVM based infrastructure to support graph lowering, hardware-aware optimizations, and performance-centric code emission. Collaborate closely with hardware architects, microarchitects, and research teams to co-design compiler strategies that align with evolving ISA and hardware constraints. Develop profiling and analysis tools to identify performance bottlenecks, validate generated code, and ensure high throughput/low latency execution of AI workloads. Enable efficient mapping of high-level ML models to hardware by working with model frameworks and graph representations such as ONNX, JAX, and PyTorch. Drive performance tuning strategies including kernel authoring, schedule generation, and hardware-specific optimization passes.
Software Intern
As a Software Engineering Intern at TensorWave, responsibilities include collaborating with senior engineers on features for cloud control plane, orchestration layer, user-facing APIs, or internal tooling; working on automation, monitoring, and observability for GPU clusters (Slurm + Kubernetes-native environments); participating in debugging performance bottlenecks in high-throughput inference or distributed training pipelines; writing clean, well-tested code and participating in code reviews; and learning how bare-metal AI clouds operate at scale, including hardware partitioning, high-speed networking, and storage.
Senior Software Engineer (Fullstack)
Lead the deployment, performance, and reliability of AI agents operating in live, high-stakes healthcare environments. Architect and scale full-stack systems integrating with EHRs, legacy healthcare platforms, and real-time voice infrastructure. Drive technical strategy for customer-facing solutions, collaborating with product and engineering to translate client needs into scalable architecture. Mentor engineers and set standards for code quality, testing, security, and HIPAA-compliant development. Shape product roadmap by identifying systemic challenges in customer workflows and proposing high-leverage technical solutions.
Senior Backend / Systems Engineer (AI) - San Mateo, CA
Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing, both for internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.
Software Engineer - Sensing, Consumer Products
As a Software Engineer on Consumer Products Research, the responsibilities include building and shipping production software for sensing algorithms by translating algorithm prototypes into reliable end-to-end systems, implementing and owning key parts of the Python shipping pipeline including integration surfaces, evaluation hooks, and quality/performance guardrails. The role also involves developing embedded/on-device software in an RTOS environment (such as Zephyr) and deploying models to device runtimes and hardware accelerators. Additional responsibilities include optimizing real-time on-device perception loops for stability, latency, power, and memory constraints, creating data collection and instrumentation tooling to bring up new sensing modalities and accelerate iteration from prototype to dataset to model to device, and partnering cross-functionally with algorithms, human data, firmware/hardware teams to debug, profile, and harden systems against real-world variability.
Senior Software Engineer, ML Core
Design, develop, and deploy custom and off-the-shelf ML libraries and toolings to improve ML development, training, deployment, and on-vehicle model inference latency. Build tooling and establish development best practices to manage and upgrade foundational libraries such as Nvidia driver, PyTorch, TensorRT, to improve ML developer experience and expedite debugging efforts. Collaborate closely with cross-functional teams including applied ML research, high-performance compute, advanced hardware engineering, and data science to define requirements and align on architectural decisions. Work across multiple ML teams within Zoox, supporting in- and off-vehicle ML use cases and coordinating to meet the needs of vehicle and ML teams to reduce the time from ideation to productionization of AI innovations.
Software Engineer, Platform Systems
Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.
Software Engineer, Platform Systems
Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.
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