Staff AI/Machine Learning Engineer

1 dia atrás

São Paulo, São Paulo, Brasil Handoff Tempo inteiro
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 As a Staff engineer, you will focus primarily on GenAI and LLM-based systems, while maintaining a strong generalist foundation across machine learning, data, and production systems. This role is ideal for a highly experienced, hands-on engineer who thrives in ambiguous problem spaces and enjoys shaping technical direction through influence rather than formal management. Your impact will come from setting standards, unblocking complex problems, guiding architectural decisions, and elevating the overall quality and velocity of ML work across the team.
What you'll do
  • Act as a technical reference for the team, supporting engineers through design reviews, technical discussions, and hands-on problem-solving.
  • Design, guide, and evolve LLM- and GenAI-based systems (e.g. AI agents, RAG pipelines, decision-support tools), balancing performance, cost, reliability, and user impact.
  • Influence the architecture and implementation of ML systems across the stack, from data pipelines and experimentation to deployment and monitoring in production.
  • Define and promote best practices and standards for model evaluation, experimentation, observability, and iteration across ML initiatives.
  • Partner closely with product and engineering to shape ML-driven solutions, clarify trade-offs, and ensure alignment with business goals.
  • Lead technically complex or ambiguous initiatives, unblocking teams and driving clarity where requirements or approaches are not well-defined.
  • Improve the maturity of ML infrastructure and workflows to support multiple contributors and use cases over time.
  • Stay up to date with advancements in GenAI, LLM tooling, and ML systems, selectively introducing new approaches where they provide clear value.
  • Share knowledge through documentation, mentoring, and collaborative problem-solving, raising the technical bar across the organization.
About you
  • Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience).
  • 8+ years of experience working with machine learning systems in production, with increasing technical scope and impact.
  • Strong generalist ML background, with depth in modern GenAI and LLM-based systems.
  • Comfortable operating in ambiguous environments, making sound technical decisions and clearly articulating trade-offs.
  • Strong communicator who can translate complex technical concepts for engineers, product partners, and non-technical stakeholders.
  • Product-minded, always grounding technical decisions in user value and business impact.
  • Thrives in a fast-paced startup environment, balancing rapid iteration with long-term technical quality.
Technical Expertise:Proficiency in foundational ML tools: Pandas, NumPy, OpenCV, and scikit-learn.Deep experience with LLMs and GenAI systems, including prompt engineering, RAG architectures, fine-tuning, evaluation, and cost/performance trade-offs.Hands-on experience with deep learning frameworks like PyTorch or TensorFlow.Experience designing, deploying, and maintaining production-grade ML systems on cloud platforms (AWS, GCP, or Azure).Strong understanding of data pipelines and ML workflows, using tools such as SQL, Apache Airflow, and cloud storage.Familiarity with computer vision techniques and tooling is a nice to have, but not required.
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

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