Machine Learning Engineer
Description For Machine Learning Engineer
SQUIRE, the leading business management platform for barbers and shop owners, is seeking a Machine Learning Engineer to join their team. Founded in 2015, SQUIRE serves 4,000+ shops across thousands of cities globally, providing essential tools for business management and customer relationships.
The role offers an exciting opportunity to work on embedding ML solutions directly into product features. As the ML Engineer, you'll be responsible for the complete machine learning lifecycle, focusing on transforming state-of-the-art ML (particularly LLMs and AWS-based solutions) into production-ready systems. This position is perfect for someone who combines strong engineering principles with practical ML application expertise.
You'll work in a hybrid setting in Buenos Aires, collaborating with cross-functional teams to deliver ML-powered features that directly impact SQUIRE's user base. The role requires 3+ years of experience in ML engineering, with a strong foundation in Python and cloud-based ML services, particularly AWS. You'll be responsible for building and maintaining scalable ML pipelines, working with off-the-shelf models, and driving productionization efforts.
The company offers competitive compensation, including salary and equity, along with the opportunity to shape AI-first engineering culture. You'll have significant autonomy while working with an exceptional engineering team. This role is ideal for someone who values practical impact over academic perfection and wants to contribute to a rapidly growing platform that serves thousands of small businesses.
Responsibilities For Machine Learning Engineer
- Own the full ML lifecycle for product features—from ideation to deployment, observability, and iteration
- Use off-the-shelf models to solve core user and business problems
- Build and maintain robust, scalable ML pipelines and infrastructure
- Work cross-functionally with product managers, designers, and backend engineers
- Drive productionization: model inference, latency tuning, monitoring, logging, and failure recovery
- Continuously evaluate and integrate emerging models to improve accuracy and capabilities
- Bring an engineering mindset to ML work—automate processes, reduce tech debt, and champion good practices
Requirements For Machine Learning Engineer
- 3+ years of experience in machine learning engineering within a product-oriented tech team
- Strong software engineering background with experience writing clean, scalable code in Python
- Hands-on experience using cloud-based ML services (especially AWS) and pre-trained models in production
- Experience with modern deployment stacks and MLOps: CI/CD, model versioning, monitoring
- A pragmatic mindset—value impact, simplicity, and reliability over academic perfection
Benefits For Machine Learning Engineer
- Competitive salary
- Equity
- Benefits package
- Work on impactful, customer-facing features
- Autonomy and ownership over domain
- Support of an exceptional engineering team
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