Top Tech Jobs & Startup Jobs

Build and maintain high-performance Apache Iceberg lakehouse infrastructure, including compaction, snapshot expiry, manifest rewriting, and orphan-file cleanup. Develop Rust-based query-routing services, connectors, and production systems with strong data integrity under concurrency and failure conditions. Review open-source contributions, debug customer environments, manage releases, and participate in on-call support.

Own and scale profitable paid mobile user acquisition across multiple ad networks. Manage budgets, ROAS targets, bidding, campaign optimization, testing roadmaps, geographies, audiences, and creatives. Analyze cohort KPIs, MMP data, SQL, and BI dashboards while partnering with Creative, Data, and Monetization teams to connect acquisition costs with ad revenue. Build network relationships, identify optimization opportunities, and report performance and growth plans to management.

Set up, restructure, launch, and optimize Google and Microsoft Search advertising campaigns. Manage keywords, search terms, negative keywords, bids, and campaign performance metrics including CPC, CPA, ROAS, and Impression Share. Use Excel and internal optimization tools to validate data, identify anomalies, test features, and provide occasional written client updates.

Build and maintain high-performance data lakehouse infrastructure, including Apache Iceberg maintenance workflows, compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Develop Rust-based SQL routing components, connectors, and query infrastructure. Ensure data integrity under concurrency and failures, contribute to open-source projects, operate production systems, participate in on-call support, and debug customer environments.

Optimize LLM inference on modern GPUs by analyzing GPU architecture, memory access, kernels, and performance bottlenecks. Develop, debug, reverse-engineer, and optimize CUDA kernels and low-level GPU operations for matrix multiplication, attention, and neural network layers. Benchmark latency, throughput, and utilization using frameworks such as vLLM, SGLang, TensorRT-LLM, Triton, and FlashAttention. Investigate technically challenging AI systems and high-performance computing problems.

Own technical support and customer onboarding for an AI-powered B2B SaaS platform. Troubleshoot Mac and Windows environments, SSO and identity providers, MDM, VPNs, desktop applications, CRM integrations, and user permissions. Lead customer IT meetings, resolve or validate issues, document bugs for R&D, train users, and manage onboarding follow-ups through go-live.

Build modular AI video-generation pipelines for mobile gaming advertisements. Integrate generative image, video, text-to-speech, and talking-head models; orchestrate fal.ai jobs with retries, webhooks, rate limits, resumability, and cost tracking. Use FFmpeg to assemble clips, subtitles, overlays, audio, and multiple aspect ratios. Document configurable workflows so marketing teams can operate them without coding, while collaborating with creative specialists and demonstrating a working pipeline.

Develop and improve computer vision systems for aerial image recognition, including object detection, segmentation, classification, and pattern recognition. Train, fine-tune, evaluate, and deploy machine learning models; build data pipelines; experiment with foundation and multimodal models; prototype new AI applications; and optimize model accuracy, reliability, performance, and inference efficiency. The role requires independent ownership from research through production deployment.

Build node-based generative AI workflows for image, video, audio, and multimodal content pipelines. Create showcase demos, integrate and test AI models, design automation flows, develop user-friendly widgets, and collaborate with AI engineers, UX designers, and product leadership. The role requires prompt engineering, model chaining, workflow automation, and hands-on experience with systems such as ComfyUI or similar node-based platforms.

Senior systems engineer focused on Rust-based low-level, high-performance Big Data infrastructure. Responsibilities include building native execution engines, deep performance tuning (SIMD, cache locality, zero-copy IO), optimizing multi-threaded systems, and accelerating distributed frameworks like Spark, Hadoop, and Ray.