Top Machine Learning Jobs in Brasilia

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Build and support backend Python and Go services, APIs, and cybersecurity detection features. Translate researcher-developed detections into reliable production workflows, expose threat evidence for customer investigations, and ensure safe retries, tenant isolation, authorization, observability, and failure handling. Collaborate with Platform Engineering, Product, frontend engineers, and security researchers while contributing to design, testing, documentation, code reviews, production support, and performance improvements.

Leads Data Scientists and Machine Learning Engineers while combining technical leadership with hands-on machine learning development. Responsibilities include architecting and deploying production ML systems, aligning data strategy with business goals, guiding experiments and statistical analysis, recruiting and mentoring talent, setting team objectives, and promoting data-driven practices. The role may also involve LLM, RAG, fine-tuning, AI agent, deep learning, and NLP initiatives.

Apoiará a pesquisa, desenvolvimento, implementação e avaliação de modelos de machine learning para agentes conversacionais e sistemas multiagente. Analisará grandes volumes de dados, realizará análises exploratórias, identificará padrões e anomalias e contribuirá para experimentos e melhorias de produto. Trabalhará com engenheiros e gerentes de produto no ciclo de vida dos modelos, do protótipo à produção, usando feedback e dados reais para iterar seu desempenho.

Monitor real-time transactions and customer behavior for fraud and abuse, investigate flagged activity, make risk decisions, clear false positives, and block fraudulent users. Analyze account patterns and fraud trends, contribute to detection models and rules, maintain case-resolution SLAs, handle escalations, and collaborate with data science, engineering, and operations teams to improve fraud prevention processes and tools.

Lead the design and delivery of high-volume, mission-critical distributed systems. Develop scalable, resilient architectures supporting low-latency conversational experiences, microservices orchestration, and real-time AI/ML processing. Guide technical execution, evaluate engineering trade-offs, improve system reliability and cost efficiency, support legacy modernization, and foster collaborative, inclusive engineering practices across teams.

Reviews real-time transactions and customer activity flagged for fraud, investigates suspicious accounts and transactions, clears false positives, and blocks fraudulent users. The intern analyzes behavior patterns, researches fraud trends, supports machine learning and detection rules, maintains SLA performance, handles escalations, and collaborates with operations, analytics, data science, and engineering teams.

Develop scalable software applications and AI-driven solutions, research emerging AI tools, prototype innovations, and collaborate with stakeholders to improve workflows and products. The role involves applying machine learning concepts, LLMs, generative AI frameworks, data preprocessing, feature engineering, and model evaluation while promoting AI knowledge sharing across teams.

Manage and grow Optibus’ Brazil customer base by leading complex B2B and B2G sales cycles, partnerships, RFPs, negotiations, and deal closures. Build client relationships, manage pipelines, exceed revenue KPIs, maintain Salesforce forecasting data, collaborate with Marketing, Product, Customer Success, and Pre-Sales teams, and represent the company at industry events. The role requires English and Portuguese fluency and national and international travel.

Staff Software Engineer responsible for designing and delivering highly scalable, resilient distributed systems supporting high-concurrency workloads, conversational AI, microservices orchestration, and real-time machine learning. The role requires technical leadership, architecture expertise, engineering trade-off analysis, collaboration, and inclusive culture building. Candidates should have 10+ years of software development experience, strong distributed systems knowledge, and fluency in Portuguese and English. Experience with LLMs, multi-agent systems, data pipelines, FinOps, or monolith modernization is advantageous.

Leads AML transaction-monitoring optimization, rule testing, threshold tuning, scenario design, and false-positive reduction. Drives FIU automation and workflow improvements, evaluates AI and LLM tools, supports alert triage and investigations, and partners with Product, Engineering, Operations, and vendors on compliance technology integrations. Helps scale monitoring frameworks across products, jurisdictions, and transaction volumes while maintaining regulatory alignment.

Owns global fraud detection strategy across markets and fraud vectors. The role develops rules, risk signals, machine learning models, automation, case management, and AI-powered detection capabilities. It establishes model governance, monitoring, KPIs, and human oversight while identifying emerging threats such as synthetic identities, deepfakes, and AI-enabled fraud. The strategist partners with Fraud Operations, Risk, Product, Engineering, and Data Science to implement scalable, continuously monitored fraud controls.

Develop and deploy predictive machine learning models for anomaly detection, forecasting, rate modeling, and invoice classification. Apply NLP, embeddings, and LLMs to supply chain documents and contracts. Build scalable data pipelines, monitoring systems, and retraining workflows; analyze transactional data; query databases with SQL; create visualizations and executive presentations; and maintain data quality, governance, security, and model performance.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Leads engineering standards, architecture governance, platform strategy, technical quality, security and compliance engineering, and production AI governance across CSL Plasma’s digital products. Manages cross-domain technical teams, sets cloud and integration direction, owns shared engineering platforms and data architecture, and ensures reliable, scalable, regulated software delivery. The role also drives AI-assisted development, production AI operations, engineering metrics, talent development, and alignment with enterprise architecture and cybersecurity.

Design and deliver AI data solutions, including labeling pipelines, quality controls, automated workflows, and AI tooling. Serve as the primary technical contact for customers, lead discovery and solution design, manage end-to-end delivery, monitor quality and financial KPIs, troubleshoot issues, and optimize scalable solutions. The role requires strong Python and machine learning knowledge, project ownership, customer communication, and the ability to explain technical concepts to varied stakeholders.

Design, develop, enhance, and support scalable backend products and services using Java. Collaborate on technical architecture, APIs, testing, code reviews, security, performance, and high-availability systems. Apply AI and LLM solutions, support data management and visualization, and work with cross-functional product teams on experimentation and business metrics.

Design, build, and deploy ML models and end-to-end pipelines; collaborate with data and MLOps teams to integrate, monitor, retrain, and optimize models; implement cloud-based scalable solutions and document processes.

Build, deploy, and operate production AI agents and features on large Databricks datasets. Collaborate with product, support, and engineering stakeholders to define requirements, ship iteratively, and own monitoring, evaluations, costs, and failure handling. The role requires strong Python, machine learning, LLM API, MCP, cloud, and batch or streaming pipeline experience, with a focus on agentic engineering, testing, and production reliability.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, data flows, APIs, integrations, and access methods for reporting, dashboards, operational use, and data science. Translates business requirements into technical solutions, manages code integration, applies data governance, cleanses data, and supports complex project planning and delivery. Works independently across multiple dependencies and provides coaching to junior associates.

Designs and operates production machine learning systems for predictive customer lifetime value. Responsibilities include feature engineering, gradient-boosted and probabilistic modeling, calibration, identity resolution, model monitoring, drift detection, data pipelines, cloud-based ML workflows, API integrations, and software engineering practices such as testing, CI/CD, and deployment safeguards.

Leads the architecture and implementation of complex AWS cloud data solutions. Defines client data strategy roadmaps, supports pre-sales engagements as a data engineering subject matter expert, mentors technical teams, interviews candidates, and establishes data standards and operating procedures. The role requires extensive experience across traditional and modern data platforms, including data modeling, databases, BI, governance, big data, machine learning, generative AI, DataOps, infrastructure automation, and CI/CD.

Develop, debug, test, and optimize machine learning models, training pipelines, inference systems, numerical methods, and supporting infrastructure. Build reproducible Python workflows, diagnose computational issues, validate implementations, optimize memory and throughput, use coding agents, and document technical findings. The role is remote worldwide and requires approximately 15 hours per week with rapid task availability after onboarding.