Senior Fullstack Engineer (Reacsystems, ranging from resource‐efficient models designed for limited hardware environments to complex, multi‐modal architectures that integrate data such as text, images, and audio.We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures.
You will adopt a hands‐on, research‐driven approach to develop, test, and implement novel serving strategies and inference algorithms.
Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments.
The ultimate goal is to enable high‐throughput, low‐latency, low‐memory footprint, and scalable AI performance that delivers tangible value in dynamic, real‐world scenarios.ResponsibilitiesDesign and deploy state‐of‐the‐art model serving architectures that deliver high throughput and low latency while optimizing memory usage.
Ensure these pipelines run efficiently across diverse environments, including resource‐constrained devices and edge platforms.
Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint.Build, run, and monitor controlled inference tests in both simulated and live production environments.
Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource‐constrained devices.
Document iterative results and compare outcomes against established benchmarks to validate performance across platforms.Identify and prepare high-quality test datasets and simulation scenarios tailored to real‐world deployment challenges, specifically those encountered on low‐resource devices.
Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions.Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics.
Address issues such as suboptimal batch processing, network delays, and high memory usage to optimize the serving infrastructure for scalability and reliability on resource‐constrained systems.Work closely with cross‐functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on‐device applications.




Define clear success metrics such as improved real‐world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements.A degree in Computer Science or related 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 applshoot issues across the frontend BFF backend stack using both traditional debugging techniques and AI‐assisted workflows.
Ensure the reliability, accessibility, and performance of agent‐critical workflows running in production.
Participate in code reviews, architecture discussions, technical decision‐making, and engineering best practices around type safety, linting, testing, and maintainability.
What You Bring
8+ years of software engineering experience, with a strong frontend‐oriented background.




Strong production experience with React and TypeScript.
Hands‐on experience actively using AI development tools and agentic workflows such as Cursor, Claude Code, GitHub Copilot, coding agents, AI‐assisted specifications, or similar tooling.
Ability to use AI as part of the engineering workflow while maintaining ownership over architecture, code quality, testing, and technical decisions.
Experience building complex product interfaces involving forms, tables, filters, dashboards, and multi‐step workflows.
Strong understanding of modern frontend development practices and component‐driven architectures.
Experience with modern bundlers such as Webpack, Rspack, or Vite.
Experience with Tailwind CSS or similar modern styling frameworks.
Experience consuming REST APIs and working with OpenAPI‐based services.
Ability to debug and troubleshoot issues across frontend and backend boundaries.
Experience with modern state management and server‐state patterns such as TanStack Query, React Context, and hooks.
Strong debugging, problem‐solving, and technical decision‐making skills.
Ability to collaborate effectively with Product, Engineering, and operational stakeholders.
Advanced English proficiency, both spoken and written.
Nice to Have
Experience designing or integrating AI‐agent workflows, AI‐powered interfaces, or automated support operations.
Experience with micro‐frontends or Module Federation.
Experience working with monorepos using pnpm, Turbo, Nx, or similar tools.
Familiarity with Refine, react‐intl, or admin/operations‐focused internal platforms.
Experience developing BFF layers with Node.js / Fastify.
Experience with Python / FastAPI, particularly the ability to understand APIs, propose schema changes, or implement small backend endpoints.
Experience working with PostgreSQL‐backed services and transactional or domain‐oriented APIs.
Experience in FinTech, payments, remittances, customer support platforms, or high‐volume operational systems.
Experience with GCP, including technologies such as OIDC or Pub/Sub.
Experience with Docker and CI/CD in cloud‐native environments.
Familiarity with Storybook, Vitest, and design‐system workflows.
Experience working in fast‐moving startup or scale‐up environments.
What Darwoft Offers
Contractor agreement with payment in USD
100% remote work
Argentina's public holidays
English classes
Referral program
Access to learning platforms
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📌 Senior Mlops Engineer — Azure, Full-Stack Advocate (Santa Fe)
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