Why join us?
Handoff is the AI agent that runs a construction company. We help remodelers automate estimating, streamline operations, and win more work - backed by real-time cost data, intuitive design, and workflows that “speak contractor.” With over 10,000 monthly active users and $6B in annualized project volume already flowing through our platform, we’re becoming the trusted partner for the people who build our homes.
We are backed by $25M+ raised from Y Combinator, Initialized, and Greycroft. Our team is distributed across hubs in Austin, São Paulo, and Buenos Aires, and we are deeply focused on building intuitive, high-impact solutions that make a real difference for our users.
Staff Machine Learning Engineer at Handoff
You’ll be the technical reference for building and scaling production-grade AI systems at Handoff. This is a hands-on, senior IC role focused on LLM- and GenAI-powered systems, with a strong foundation in software engineering and distributed systems.
You’ll shape technical direction through influence - defining standards, unblocking complex problems, guiding architecture, and raising the bar for reliability, observability, and maintainability across our AI initiatives. You’ll work closely with Product, Core Engineering, and Data to deliver AI capabilities that are tightly aligned with real contractor workflows and measurable business outcomes.
What you'll do
Act as a technical reference for AI engineering, supporting the team through design reviews, architecture discussions, and hands-on problem-solving.Design, guide, and evolve LLM- and GenAI based systems (agents, RAG pipelines, tool calling, decision-support workflows), balancing quality, latency, cost, reliability, and user impact.Lead or heavily influence at least one high-impact AI system from design → launch → iteration, with measurable gains in quality and reliability.Define and promote best practices for AI system architecture, evaluation, deployment, monitoring, and iteration.Design observability and reliability patterns for AI systems, including tracing, logging, metrics, alerting, error taxonomies, and cost/performance tracking.Improve the maturity of AI engineering infrastructure and workflows, including CI/CD, deployments, feature flags and experiments, prompt and configuration versioning, scalable inference patterns, and feedback loops.Partner closely with Product and Engineering leadership to shape roadmap feasibility and technical trade-offs (latency, cost, quality, safety, UX).Translate new research, tools, or emerging techniques into concrete experiments and production-ready systems only when they create clear product value.Mentor and unblock other engineers through pairing, shared debugging, architecture reviews, and documentation — raising overall quality and time-to-ship across AI initiatives.
About you
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).8+ years of experience building and operating machine learning systems in production, with increasing scope and impact.Strong software engineering foundation, with experience owning production services end-to-end (APIs, distributed systems, async workflows).Deep hands-on experience with LLM and GenAI systems, including prompting, tool calling, agentic architectures, RAG systems, and evaluation.Proven ability to build reliable, observable AI systems, designing for failure and safe iteration in production.Comfortable working with data and evaluation pipelines (SQL, events, feedback loops) without requiring a research-first background.Sound technical judgment and strong communication skills, able to explain trade-offs clearly to engineers, PMs, and stakeholders.Product-minded, grounding technical decisions in real user value and measurable outcomes.Experience mentoring engineers and raising the technical bar through reviews, standards, and hands-on support.Thrives in fast-paced startup environments with high ownership and ambiguity.B2+ English proficiency, both written and verbal.
If you enjoy shaping the technical direction of AI systems, tackling ambiguous problems, and using GenAI to deliver meaningful user and business impact, we’d love to hear from you!
