Top Tech Jobs & Startup Jobs in Los Angeles, CA

Lead design and training of large-scale transformer and hybrid foundation models across text and multimodal domains. Own architecture choices, diagnose large-scale training instabilities, optimize scaling tradeoffs (data, compute, model), design loss and alignment strategies, and execute distributed training (FSDP/ZeRO, tensor/pipeline parallelism) with mixed precision to build in-house foundation model expertise.

Lead development and optimization of high-performance LLM inference pipelines across data center, edge, and embedded platforms. Improve latency and throughput via custom CUDA kernels, quantization, KV-cache and batching strategies, and deploy efficient, production-ready runtimes without external vendor dependency.

Customer-facing sales role responsible for owning direct opportunities and managing channel/distributor relationships to drive revenue. Duties include discovery, proposals, demos, negotiation, pipeline management, partner enablement, co-selling, event representation, and cross-functional coordination to support go-to-market and product launches. Targets include bookings, distributor-sourced pipeline growth, and executing partner workshops.

Design and implement advanced generative AI models, optimize deployment in production, collaborate with teams, and stay updated on AI advancements.

Design, build, and operate distributed GPU training infrastructure for large neural networks. Optimize multi-node/multi-GPU execution, diagnose compute/memory/network bottlenecks, improve stability and fault tolerance, and partner with research teams to productionize large-model training pipelines for reliable, high-throughput training at scale.

Design and implement advanced Generative AI models, optimize performance for LLMs, and collaborate with cross-functional teams to innovate AI solutions within the automotive sector.

Lead design and training of large-scale transformer and hybrid foundation models across text and multimodal domains. Drive architecture choices (attention variants, MoE), optimize training stability and optimizers, validate scaling laws and compute/data tradeoffs, design loss and alignment strategies (RLHF, DPO, contrastive), and execute distributed training (FSDP, ZeRO-3, tensor/pipeline parallelism) with mixed precision and checkpointing while partnering with ML systems teams.

Design, build, and maintain FinOps tooling, data pipelines, dashboards, and automation to improve cloud cost visibility, allocation, and optimization across Azure, AWS, and GCP. Support budgeting, variance analysis, anomaly detection, and executive reporting. Collaborate cross-functionally to implement tagging, chargeback/showback, governance, and AI-enabled automation while documenting operational processes for scalable FinOps practices.