ML Engineer

Next Ventures; São Paulo; Presencial; BRL 80.000 - 120.000; Tempo integral

Resumo da oferta

A leading tech company in São Paulo is seeking a Senior Machine Learning Engineer to drive the development and deployment of machine learning systems. You will work on end-to-end ML models and services, collaborate with cross-functional teams, and ensure high-quality implementations. The ideal candidate should have strong analytical skills, advanced Python expertise, and experience managing the entire ML lifecycle. This role offers an impactful position in the machine learning domain, with opportunities for growth and development.

Qualificações

• 5+ years of professional Python development focused on data science and ML.
• 3+ years building and deploying end-to-end ML solutions in production.
• 2+ years working with structured multivariate time-series datasets.

Responsabilidades

• Own the entire ML lifecycle: problem framing, exploration, modeling, evaluation, deployment, monitoring.
• Communicate complex ML concepts to technical and non-technical audiences.
• Mentor junior data scientists and engineers to elevate team capabilities.

Conhecimentos

Strong analytical and problem-solving skills

Advanced Python expertise

Experience deploying tree-based models

Hands-on experience with structured multivariate time-series data

Proficiency with Linux, Git, and Bash

Experience with CI/CD pipelines

Familiarity with monitoring tools for ML systems

Comfort working with SQL and relational databases

Experience integrating LLM APIs

Strong communication skills

Formação académica

Bachelor's, Master's, or Ph.D. in a quantitative discipline

Ferramentas

Docker

Kubernetes

NumPy

pandas

Scikit-learn

PyTorch

Jupyter

FastAPI

MLflow

TensorBoard

Descrição da oferta de emprego

Senior Machine Learning Engineer

Join a team focused on building production-grade machine learning systems that power data-driven decision-making across large, complex datasets. In this role, you will own ML models and services end-to-end—from exploration and prototyping through deployment, monitoring, and continuous improvement—while collaborating closely with data science, product, engineering, and operations teams.

What You’ll Do

ML Design & Development

• Architect, implement, and maintain machine learning models—including gradient-boosted trees, neural networks, forecasting models, and transformers.

• Use Python and the modern data science ecosystem (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter, visualization tools).

Data Analysis & Feature Engineering

• Explore and analyze large structured datasets, particularly multivariate time-series and billing/operational data.

• Engineer high-quality features, assess data assumptions, and iterate to improve model performance.

Production Systems & APIs

• Develop clean, scalable code and internal APIs (e.g., FastAPI) for both online and batch inference.

• Integrate ML services into existing systems and workflows.

Code Quality & Engineering Practices

• Apply best practices in version control, documentation, code reviews, and test-driven development.

• Ensure reliability, clarity, and long-term maintainability of ML codebases.

MLOps, CI/CD & Observability

• Design and manage CI/CD pipelines for ML workloads (e.g., GitHub Actions).

• Build and maintain containerized deployments using Docker and Kubernetes (or similar tools).

• Implement monitoring, logging, and experiment tracking with platforms such as MLflow, TensorBoard, Datadog, Neptune, or Weights & Biases.

Data Engineering Collaboration

• Work with relational and analytical data stores (Postgres, parquet, DuckDB).

• Partner with data engineering teams on SQL/dbt-based pipelines for training, validation, and production scoring.

LLM Integration

• Use LLM APIs and tooling (e.g., OpenAI, Cursor) to integrate large language models into products, workflows, and pipelines where they deliver measurable value.

Lifecycle Ownership & Continuous Improvement

• Own the entire ML lifecycle: problem framing, exploration, modeling, evaluation, deployment, monitoring, retraining, and decommissioning.

• Identify technical debt and drive ongoing improvements in performance and reliability.

Collaboration, Communication & Mentorship

• Communicate complex ML concepts to both technical and non-technical audiences.

• Document findings and architectural decisions.

• Mentor junior data scientists and engineers to elevate team capabilities.

What You’ll Bring

• Strong analytical and problem-solving skills grounded in machine learning principles.

• Advanced Python expertise and deep knowledge of the data science ecosystem (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter).

• Experience deploying tree-based models and deep learning models in production.

• Hands-on experience with structured multivariate time-series data.

• Proficiency with Linux, Git, Bash, and cloud or high-performance computing environments.

• Experience with CI/CD pipelines, Docker, and Kubernetes for ML workloads.

• Familiarity with experiment tracking, monitoring, and logging tools for ML systems.

• Comfort working with SQL, relational databases (e.g., Postgres), and analytical formats/engines (parquet, DuckDB).

• Experience integrating and prompting LLM APIs for data and workflow automation.

• Strong written and verbal communication skills and a track record of effective cross-functional collaboration.

• Interest in mentoring others and improving engineering/ML practices across the team.

Minimum Qualifications

• Bachelor’s, Master’s, or Ph.D. in a quantitative discipline—or equivalent experience demonstrating senior-level ML engineering capability.

• 5+ years of professional Python development focused on data science and ML.

• 3+ years building and deploying end-to-end ML solutions in production.

• 3+ years working with deep learning or decision-tree-based methods.

• 2+ years working with structured multivariate time-series datasets.

• Demonstrated experience with:

  • CI/CD for ML workloads

  • Docker and Kubernetes (or similar orchestration)

  • Linux-based cloud or high-performance training environments

Preferred Qualifications

• Ph.D. or equivalent research experience in advanced ML.

• Experience with logistics, supply chain, or operational datasets.

• Deep expertise in transformers, advanced forecasting methods, or unsupervised learning for structured data.

• Publications, conference talks, or notable open-source contributions demonstrating ML innovation.

• Experience building LLM-powered tools or applications using APIs and modern LLM frameworks.