Machine Learning
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You build and ship machine learning and AI features in production. Some weeks you are building retrieval over a large language model; others you are hardening an inference pipeline.
Why this role exists
Our client is a funded AI-native startup that builds products on top of machine learning and large language models. They ship real AI features to production and need engineers who can take models from prototype to reliable, scalable systems.
What you'll own
- Machine learning and large language model features: built, trained, fine-tuned or integrated
- The data and inference pipelines that put models into production
- Model evaluation, with improvements to accuracy, latency and cost
- AI features shipped end to end with product and engineering
- Production monitoring, fixing drift, errors and bottlenecks
- Staying current with the AI tooling landscape and applying what is useful
Why this is interesting
- Real AI features that reach users, not just prototypes
- Ownership of models and pipelines in production
- A funded AI-native startup working with large language models, retrieval and agent systems
What we're looking for
- 3+ years building machine learning or AI systems, with work that reached production
- Strong in Python and the modern AI stack (large language model APIs, retrieval, frameworks such as LangChain, vector databases)
- Solid grasp of data pipelines, evaluation and the engineering around models, not just notebooks
- You ship reliable systems and take ownership of what you build
- English C1, written and spoken. You work comfortably with a US team
Bonus
- Fine-tuning, retrieval-augmented generation or agent systems in production
- Cloud and MLOps experience (AWS, GCP, containers, pipelines)
- Degree in computer science, data or a related field
What success looks like
First 90 days
You understand the current ML architecture, data pipelines and production models. You have shipped one feature or hardened one pipeline end to end, and you are trusted to own a model or pipeline without constant review.
Ongoing
You regularly ship AI features that improve the product and reach users. Models you own stay reliable in production with minimal drift or errors, and you improve accuracy, latency or cost on at least one system per quarter.
Why Puente
Puente connects experienced Latin American professionals with growing US companies. We do not operate as a generic job board: we selectively match candidates with active roles and guide them through the hiring process.
3% acceptance rate · 2,873+ professionals placed · 97% retention beyond 12 months · 18 countries · 100% remote
Hiring process
- Puente screening: a WhatsApp intro, then a recruiter interview
- Client interview with the hiring manager
- Background check and offer
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