Senior/Staff Software Engineer (AI-Native/Claude Code/Codex) · LATAMLocation: Anywhere in LATAMJob Type: 100% RemoteEngagement: Full-time ContractorPayment: USDProject: AI-Native LegalTech / M&A; SaaS PlatformSeniority: Senior / StaffEnglish Level: B2 / C1Get to Know DarwoftAt Darwoft, we build digital products with heart.
We're a Latin American technology company focused on creating impactful, human-centered software in partnership with companies around the globe.Our remote-first culture is built on trust, continuous learning, collaboration, and strong engineering standards.
We work with international teams solving complex product and technology challenges where our engineers can have a direct impact on the product.If you're looking for a place where your technical judgment matters and where you can contribute beyond simply executing tickets, welcome to Darwoft.About the OpportunityWe're partnering with an innovative software company building a modern transaction platform for organizations managing complex M&A; processes.The platform brings together document workflows, collaboration, approvals, diligence, transaction management, and AI-powered capabilities within a single SaaS product.What makes this opportunity particularly different is the way the engineering team builds software.The product has been developed using an AI-native engineering approach, with tools such as Claude Code, OpenAI Codex, and other AI coding agents deeply integrated into the development workflow.We're looking for a highly experienced Senior / Staff Software Engineer who can combine strong software engineering fundamentals with this new way of building software.This is not a traditional AI/ML Engineer role.You won't be training machine learning moderelated field.
Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D; (with good publications in A* conferences).




Must have knowledge of Metal Shading Language (MSL).
You should be comfortable writing custom compute shaders from scratch.Proven experience in low‐level kernel optimizations and inference optimization on mobile devices is essential.
Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain‐specific applications, particularly on resource‐constrained devices and edge platforms.A deep understanding of modern model serving architectures and inference optimization techniques is required.
This includes state‐of‐the‐art methods for achieving low‐latency, high‐throughput performance, and efficient memory management in diverse, resource‐constrained deployment scenarios.Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines.
Practical experience in developing and deploying end‐to‐end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource‐constrained devices is required.Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints.
You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.Distributed Inference Systems: Designing and optimizing high‐performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters.Deep understanding of the math and structure behind Diffusion Models and Vision TransformersUnderstanding of Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc.
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📌 Senior Software Engineer – Open Source & Swe-Bench Evaluation (Santa Fe)
🏢 Anyone AI
📍 Santa Fe