Senior/Staff Software Engineer (AI-Native/Claude Code/Codex) · LATAM
Location: Anywhere in LATAM
Job Type: 100% Remote
Engagement: Full-time Contractor
Payment: USD
Project: AI-Native LegalTech / M&A; SaaS Platform
Seniority: Senior / Staff
English Level: B2 / C1
Get to Know Darwoft
At 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 Opportunity
We'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 models or working primarily on Data Science.
Instead, you'll be engineering production software, navigating a large existing codebase, making architectural decisions, improving r empower individuals to thrive in the digital and gig economies, driving integral growth and opportunity.Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.Why Join Us?
Our team is a global talent powerhouse, working remotely from every corner of the world.
If you're passionate about making a mark in the fintech space,



this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards.
We've grown fast, stayed lean, and secured our place as a leader in the industry.If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.Are you ready to be part of the future?
About the jobAs a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems.
Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real‐world applications.
You will work on a wide spectrum of systems, 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, comalTech, FinTech, transaction platforms, Private Equity, or other complex B2B SaaS products.
Experience improving engineering productivity through developer tooling and automation.
What Success Looks Like
Success in this role goes beyond writing more code.
You'll help the team ship significantly faster while simultaneously strengthening the architecture, reliability, maintainability, and technical maturity of the platform .
The goal is to combine strong engineering judgment with AI-native development practices to dramatically increase what a small engineering team can build.
What Darwoft Offers
Contractor agreement with payment in USD
100% remote work
Argentina's public holidays
English classes
Referral program
Access to learning platforms
Explore this and other opportunities at:
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