Docket

Senior Machine Learning Engineer

Docket

full-time

Posted on:

Location Type: Hybrid

Location: São Paulo • Brazil

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Job Level

Senior

Tech Stack

AWSCloudDockerGoogle Cloud PlatformPostgresPythonSQL

About the role

  • Develop, train, and optimize Transformer-based models and LLMs for text processing tasks;
  • Build robust text preprocessing and postprocessing pipelines;
  • Apply software engineering best practices in Machine Learning projects;
  • Work with structured and unstructured data, integrating models with databases and APIs;
  • Contribute to technical decisions on architecture, model versioning, testing, and deployment;
  • Collaborate with data engineers and other teams to scale solutions in production.

Requirements

  • Python: Advanced proficiency in the language, with emphasis on object-oriented programming, code optimization, and project organization;
  • Machine Learning: Experience with models such as BERT, RoBERTa, GPT, Mistral, including fine-tuning and application to NLP tasks. Strong experience with prompt engineering;
  • Text Processing: Classification and entity extraction;
  • OCR: Basic experience with tools such as Tesseract and Google Vision OCR;
  • Databases (PostgreSQL): Data manipulation using SQL, integration with ML pipelines;
  • Git/GitHub: Code versioning, PR reviews, use in collaborative teams;
  • Debugging and Testing: Ability to identify and fix bugs, create automated tests with Pytest or Unittest;
  • Design Patterns: Application of architecture best practices and design patterns;
  • LLMs (e.g., GPT, Mistral, Claude Sonnet, etc.);
  • Preferred:
  • Experience with RESTful APIs and FastAPI;
  • Knowledge of Docker and deploying models to the cloud;
  • Familiarity with prototyping tools such as Streamlit;
  • Experience with cloud platforms (AWS, GCP);
  • Experience with LLM-based agents;
  • Use of MLflow, Weights & Biases, DVC, or similar tools for experiment tracking, versioning, and automation;
  • MLOps (Machine Learning Operations): Familiarity with practices and tools that automate and facilitate the ML lifecycle, from development to deployment and monitoring of models.
Benefits
  • Meal and food allowance via Flash for when hunger strikes.
  • Health and dental insurance.
  • Life insurance.
  • Pharmacy benefit to save on medications and support your health.
  • Petlove benefit — at Docket we understand that your furry family members matter too.
  • Psicologia Viva: access to psychological support.
  • Wellhub and TotalPass for fitness and wellness.
  • Edupass for learning and professional development.
  • Partnership with Sesc for leisure and cultural activities.
  • Childcare assistance for parents with children up to 5 years old.
  • Baby Cash benefit when your family grows.
  • Day off during your birthday month to celebrate.
  • Working hours: 44 hours per week
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonMachine LearningBERTRoBERTaGPTMistralText ProcessingOCRSQLMLOps
Soft Skills
collaborationtechnical decision makingproblem solving