Senior ML Engineer - NYC / Sao Paulo

Senior Machine Learning Engineer

About the Role
We’re looking for an experienced Machine Learning Engineer to design, build, and scale intelligent data products that support real-world applications. This position bridges data engineering, model development, and productization — transforming large datasets and unstructured information into production-ready systems.

You’ll work closely with engineering, product, and data teams to create end-to-end ML pipelines, optimize model performance, and drive experimentation with advanced NLP and LLM techniques. This role suits someone who thrives on building reliable, high-impact machine learning infrastructure from the ground up. 

• Design and maintain scalable data ingestion and transformation pipelines (ETL/ELT) to feed analytical and ML systems.

• Build, train, validate, and deploy machine learning models using reproducible workflows, CI/CD, and automated retraining pipelines.

• Implement and maintain MLOps best practices, including packaging, orchestration, deployment, and performance monitoring in production.

• Define and manage data schemas, feature stores, and metadata governance to ensure data reliability and accessibility.

• Develop data products and APIs that expose ML insights to other systems and business stakeholders.

• Monitor and continuously optimize system performance, scalability, and cost efficiency across cloud environments.

• Establish operational standards for observability, including logging, alerting, data lineage, and documentation.

• Collaborate with product and business teams to define success metrics and ensure measurable impact.

• Mentor and guide engineers and data scientists on ML system design and deployment practices.

• 7 years of experience in machine learning engineering, data engineering, or applied data science.

• Degree in Computer Science, Engineering, Mathematics, Statistics, or equivalent hands-on experience.

• Advanced proficiency in Python and SQL, writing clean, testable, and production-grade code.

• Strong understanding of software testing and data validation workflows.

• Experience working with modern cloud data platforms such as Databricks, Snowflake, or BigQuery.

• Familiarity with data modeling and transformation frameworks (e.g., dbt).

• Expertise in ML frameworks like scikit-learn, PyTorch, or Transformers, including model fine-tuning and optimization.

• Proven track record implementing MLOps workflows with CI/CD, containerized deployments (Kubernetes or serverless), and monitoring tools (e.g., MLflow, Ray Serve).

• Experience with natural language processing (NLP) techniques: prompt engineering, tokenization, regex, and libraries like spaCy or NLTK.

• Solid grasp of data integration via REST APIs.

• Ability to design reliable, scalable, and observable systems for production use.

• Experience mentoring peers or leading small technical projects.

• Experience deploying ML models at scale, optimizing for latency and cost.

• Previous work in tech, consulting, or startup environments with fast delivery cycles.

• Familiarity with financial or risk modeling domains (e.g., insurance, pricing, or forecasting).

• Experience with visualization tools such as Looker, Tableau, or Power BI.

• Knowledge of vector databases (e.g., LanceDB) and Retrieval-Augmented Generation (RAG) systems.

• Familiarity with data labeling tools like Label Studio.

You’ll have the chance to shape the data and ML foundation of a growing product ecosystem, influence architecture decisions, and experiment with cutting-edge technologies in NLP and generative AI — all within a small, high-impact team focused on measurable business outcomes.