Top Tech Jobs & Startup Jobs in Austin, TX

Train and evaluate machine learning models that improve large language model inference efficiency and capability. Responsibilities include speculative decoding, quantization, sparsity, distillation, pruning, parameter-efficient fine-tuning, agentic systems, benchmarking, research implementation, and reinforcement learning or post-training. The role collaborates with performance, compiler, runtime, and product teams to ship model improvements.

Develop and optimize compiler infrastructure for SambaNova’s RDU AI accelerator. Responsibilities include implementing IR passes, lowering and partitioning machine learning models, tensor tiling, on-chip memory management, fabric mapping, and low-level debugging in C++ and Python. The role collaborates with runtime and hardware teams and includes mentorship while building production compiler software.

Build and operate the machine learning inference stack for SambaNova’s RDU accelerators. Develop production Python features including speculative and constrained decoding, tool calling, prompt caching, long-context inference, and vLLM integration. Maintain public inference APIs, create accuracy and regression tests, and troubleshoot latency, throughput, and reliability issues. The role requires strong systems and programming fundamentals, experience with LLM serving frameworks and inference techniques, and PyTorch knowledge.

Build and maintain cloud and on-premises infrastructure supporting SambaNova’s AI inference platform. Responsibilities include managing Kubernetes clusters, Terraform and Ansible automation, monitoring and dashboards, CI/CD pipelines, regional infrastructure deployment, capacity support, and incident response. The role also participates in an on-call rotation and requires programming or scripting, cloud, networking, Linux, containerization, infrastructure-as-code, and monitoring experience.

Develop runtime software across kernel drivers, user-space libraries, networking, and system tools for SambaNova AI hardware. Build and test features on large multi-node systems, diagnose performance bottlenecks, and collaborate with hardware and compiler teams. The role requires C, C++, Rust, Python, Linux systems programming, high-speed networking, distributed communication libraries, and debugging experience.

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

Brings up and optimizes foundation models on SambaNova’s AI platform. Profiles performance across compiler, runtime, and hardware layers; identifies bottlenecks; applies quantization, kernel fusion, scheduling, and memory optimizations; benchmarks inference throughput and latency; and collaborates with machine learning, compiler, runtime, and hardware teams to deliver product improvements.

Own full-lifecycle recruiting for go-to-market and product roles across Sales, Marketing, Customer Success, and Product. Partner with hiring managers to define roles, build sourcing strategies, calibrate pipelines, design interviews, manage feedback, and close passive talent. Maintain accurate recruiting data and metrics in Greenhouse while influencing senior leaders and using AI to improve recruiting workflows. This is a six-month contract with potential extension or conversion.

Own and operate a global AI inferencing platform: ensure uptime, low-latency performance, scalability and cost efficiency. Build monitoring, alerting, CI/CD, IaC, autoscaling, capacity planning, incident response, and participate in a shared on-call rotation to maintain 24/7 service reliability.

Design and implement RTL components for AI accelerator ASICs from microarchitecture through tape-out and silicon bring-up. Responsibilities include writing and debugging SystemVerilog, optimizing power, performance, and area, supporting synthesis and timing closure, and applying lint, CDC, LEC, and assertion-based quality checks. The engineer collaborates with architecture, verification, and physical design teams while contributing to compute datapath, on-chip network, and memory subsystems.