Software Engineer, ML & Data Infra

<div class="content-intro"><h3><strong><span style="font-family: arial, helvetica, sans-serif;">About xAI</span></strong></h3> <p><span style="font-family: arial, helvetica, sans-serif;">xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. </span><span style="font-family: arial, helvetica, sans-serif;">Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. </span><span style="font-family: arial, helvetica, sans-serif;">We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. </span><span style="font-family: arial, helvetica, sans-serif;">All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.</span></p></div><h3>About the Role</h3> <p>The ML and Data Infrastructure team is responsible for building the foundational infrastructure that powers frontier AI models and truth-seeking agents—from petabyte-scale data acquisition and multimodal crawling, to web-scale search/retrieval systems, reliable high-throughput inference serving, low-level GPU/kernel optimizations, compiler/runtime innovations, and high-speed interconnect fabrics for massive clusters. In this role, you will collaborate across pre-training, multimodal, reasoning, and product teams in a fast-paced, meritocratic environment where you will tackle ambiguous, high-stakes problems with first-principles thinking and rigorous execution.</p> <h3>Responsibilities</h3> <ul> <li>Design, build, and operate petabyte-to-exabyte scale distributed systems for data acquisition, web crawling, preprocessing, filtering/classification, and multimodal pipelines (CPU/GPU workloads).</li> <li>Architect high-performance search/retrieval engines (vector/hybrid/semantic) at trillion-document scale, integrating with LLMs/agents for truth-seeking, low-hallucination reasoning, and real-time knowledge access.</li> <li>Develop reliable inference serving infrastructure: load balancing, autoscaling, KV cache, batching, fault-tolerance, monitoring (Prometheus/Grafana), CI/CD (Buildkite/ArgoCD), and benchmarking for 100% uptime and optimal tail latency.</li> <li>Optimize low-level performance: CUDA kernels (GeMM, attention), Triton/CUTLASS extensions, quantization/distillation/speculative decoding, GPU memory hierarchy, and model-hardware co-design for next-gen architectures.</li> <li>Innovate on compilers/runtimes (JAX/XLA/MLIR, custom features for Hopper/Blackwell), distributed profiling/debugging tools, and interconnect fabrics (copper/optical, 1.6T+, SerDes/photonics, topology simulation, vendor roadmaps).</li> <li>Manage complex workloads across clouds/clusters: orchestration (Kubernetes), data bookkeeping/verifiability, high-speed interconnect validation, failure analysis, and telemetry/automation for production reliability.</li> </ul> <h3>Required Qualifications</h3> <ul> <li>Strong systems engineering skills with proven impact on large-scale distributed infrastructure (data processing, search, inference, or cluster networking).</li> <li>Proficiency in Python and at least one compiled language (Rust, C++, Go, Java); experience building bespoke libraries, optimizing performance, and debugging complex systems.</li> <li>Hands-on experience with at least one key area: petabyte-scale data pipelines/crawling (Spark/Ray/Kubernetes), web-scale search/retrieval (vector DBs, ranking, RAG), inference optimization (SGLang, kernels, batching), compiler features (JAX/XLA), or high-speed interconnects (optical/copper, SerDes, signal integrity).job</li> <li>Deep understanding of distributed systems ... (truncated, view full listing at source)