ML Engineer

Member of Technical Staff – ML Performance

The role involves engineering work focused on making machine learning systems performant at scale. This includes contributing to open‑source projects and enhancing Modal's container runtime to improve the throughput and reduce the latency of language and diffusion models.

Develop, train, and optimize machine learning models for various mobile app features. Research and implement state‑of‑the‑art AI techniques to improve user engagement and app performance. Collaborate with cross‑functional teams to integrate AI‑driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data‑driven decision‑making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

Collaborate directly with the GTM team, including Account Executives and Solutions Architects, to ensure smooth integration and successful deployment of machine learning solutions. Build and present compelling demonstrations and proof of concepts that showcase AI technology capabilities. Design, develop, and deploy end‑to‑end AI‑powered applications tailored to customer needs. Contribute to the internal machine learning platform by adding features and fixing bugs. Integrate and enable new machine learning models into the existing platform or client environments. Improve system performance, efficiency, and scalability of deployed models and applications. Work closely with partners to enable joint AI solutions and ensure seamless collaboration.

Advance inference efficiency end‑to‑end by designing and prototyping algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference. Implement and maintain changes in high‑performance inference engines such as SGLang‑ or vLLM‑style systems and Together's inference stack, including kernel backends, speculative decoding like ATLAS, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post‑training pipelines with methods such as RLHF, RLAIF, GRPO, DPO‑style methods, and reward modeling, optimizing these workloads with inference‑aware training loops. Use these pipelines to train, evaluate, and iterate on frontier models on top of the inference stack. Co‑design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, identifying bottlenecks across training engines, inference engines, data pipelines, and user‑facing layers. Run ablations and scale‑up experiments to understand trade‑offs among model quality, latency, throughput, and cost and feed insights into the design process. Profile, debug, and optimize inference and post‑training services under production workloads. Drive roadmap items requiring engine modification, including changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks for rigorous validation of improvements. Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training. Mentor other engineers and researchers on full‑stack ML systems work and performance engineering.

Freelance n8n Workflow Developer – AI Trainer

Design, build, and evaluate advanced workflows in self‑hosted n8n environments. Architect multi‑system integrations for scalable automation pipelines. Develop and optimize AI‑powered workflows such as content generation, automation pipelines, and enrichment systems. Build and maintain lead generation, outreach, and data processing automation systems. Implement web‑scraping workflows and ensure reliable data extraction and processing. Optimize workflow execution, node sequencing, and error handling to prevent failures, delays, and API timeouts.

Build and deploy AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading regular demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Set up telephony systems such as SIP, CCaaS, and PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Write and refine prompts for large language model‑driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions, work consultatively to resolve challenges in security, connectivity, or knowledge ingestion. Collaborate with product and engineering teams to elevate platform gaps, resolve technical issues, and lead client implementations independently.

$108,000 – $170,000