The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems
  
  
  

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-BRAZIL

Why You'll Love This Role: 

We're looking for an experienced ML Ops Engineer to join the ML/AI team at Newsela. This team works on projects ranging from classical Machine Learning to AI / Generative pipelines.  This is a hands-on role. You'll work closely with ML/AI, data and site reliability engineers to take models from prototype to production, build robust data pipelines, and keep our services running smoothly as we continue to scale.

What You'll Be Doing:

• Design and maintain CI/CD pipelines for ML model training, packaging, and deployment across our microservices.
• Manage containerized services on AWS ECS, optimizing for cost, latency, and availability.
• Automate infrastructure provisioning and service configuration with Terraform.
• Work to maintain and scale services that make use of third party LLM providers.
• Build and improve data pipelines that feed models from BigQuery, S3, and DynamoDB into training and inference workflows.
• Instrument services with observability tooling (Datadog, OpenTelemetry, Langfuse) and establish SLOs for model-serving endpoints.
• Collaborate with ML engineers to productionize new models using BentoML, FastAPI, and container-based serving.

About You:

• 2-3 years in ML Ops supporting ML/AI features, systems and workflows with 3-4 years prior experience in DevOps, CloudOps or SRE.
• Strong proficiency in Python.
• Hands-on experience with Docker containerization and container orchestration.
• Solid understanding of CI/CD for ML workflows in an enterprise production environment.
• Experience with Infrastructure as Code, preferably Terraform.
• Familiarity with cloud platforms — specifically AWS (ECS, ECR, S3, DynamoDB, CloudWatch) and GCP (BigQuery, Vertex AI).
• Experience with LLM integration and observability (OpenAI API, Google GenAI, Langfuse tracing).
• Experience building and maintaining data pipelines for ML training and feature engineering
• Familiarity with ML modeling workflows — training, evaluation, experiment tracking (e.g. MLFlow, Weights & Biases), and model versioning
• Experience monitoring and flagging model drift over time.
• Exposure to NLP/NLU models and frameworks such as Hugging Face Transformers, spaCy, or sentence-transformers
• Knowledge of vector databases (LanceDB, FAISS) and embedding-based retrieval systems
• Experience with scaling and maintaining deep learning frameworks (TensorFlow, PyTorch) in production settings
• Familiarity with classical ML libraries (scikit-learn, XGBoost, LightGBM) and model explainability tools (SHAP)
• Working knowledge of ML serving frameworks such as BentoML or similar.
• Comfort working with FastAPI or similar async Python web frameworks.

Please note that given the nature of the contract, this role will not be eligible to participate in company-sponsored benefits

About Newsela:

Newsela is a leading education technology company dedicated to meaningful classroom learning for every student. We deliver integrated, AI-powered solutions designed to unlock student engagement, empower teachers, and drive meaningful learning outcomes. Our suite of products supports knowledge and skill development, writing practice, daily instruction, assessment, and data-informed decision-making across K–12 classrooms. Grounded in learning science research, Newsela’s solutions integrate content, assessment, and analytics to help educators track progress, understand student outcomes, and deliver high-impact instruction that supports every learner.

#LI-Remote

The Mission

This is a senior role for someone who leads from both directions at once — deeply technical on customer engagements, and fully accountable for the growth and performance of a team of ML engineers and architects. You will report to the Director of AI/ML.

You own hiring, development, and team health alongside leading complex customer engagements, shaping architecture, and driving pre-sales. Both parts of this job are real and ongoing. The right candidate will find energy in that combination, not tension.

Your Assignment

Leading Your Team

• Hire and build: Set the technical bar for ML roles on your team, lead or oversee technical assessments, and make hiring decisions you can stand behind. Build a team that raises the practice's overall standard.
• Develop people: Run regular structured 1:1s, provide candid feedback at meaningful milestones, and actively invest in each person's growth — whether they are early in their career or highly experienced.
• Manage performance: Recognize strong contributors and address performance gaps directly and early. Partner with HRBPs and the Director of AI/ML when situations require a structured path, and advocate for your team when they deserve it.
• Stay close to staffing: Understand how your team is utilized across engagements, keep the staffing team informed of each person's skills evolution and preferences, and ensure people are placed in work that stretches them appropriately.

Strategic Advisory

• Lead ML assessments: Evaluate customer environments end-to-end — infrastructure, data pipelines, model lifecycle, and organizational readiness — and produce recommendations that drive executive decisions and open the door to the next engagement.
• Shape architecture: Serve as the senior technical authority on engagements, setting architectural direction, ensuring technical quality across the team, and making the calls that matter when tradeoffs are hard.
• Advise on ML operations: Help customers build ML systems they can actually own and sustain — translating MLOps, LLMOps, and production monitoring complexity into standards their engineering teams can execute and their leadership can act on.
• Drive pre-sales: Partner with sales and solutions teams during scoping and proposal phases, contributing the technical depth needed to scope work accurately and give prospects confidence in Caylent's ability to deliver.

Hands-On Delivery

• Lead engagements end-to-end: Drive architecture and solution design from kickoff through delivery — setting technical direction, unblocking the team on hard problems, and ensuring the work meets Caylent's quality standards.
• Own the technical relationship: Depending on the engagement, you are either the primary client contact owning all architect-level outcomes, or the senior technical authority providing oversight across the team. The expectation is the same in both cases — you are the person the engagement depends on technically.

Growing the Practice

• • Raise the bar internally: Mentor engineers and architects through real work, contribute to technical interviews, and build reference architectures and accelerators that make the broader ML practice better.

Your Qualifications (non-negotiables)

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Demonstrated people management experience — hiring, performance calibration, career development, and the ability to have difficult conversations directly and constructively.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether classical ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization, and distributed training — combined with RAG and agentic system design, including multi-agent architectures, MCP integration, and human-in-the-loop patterns on AWS.
• Proven ability to operate independently in complex, ambiguous customer environments — navigating competing priorities, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Strong differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
• Fluency in AIOps patterns — designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms — and the ability to translate AI operations outcomes into measurable business value for customers.

Technical Stack

Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it.

• ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI
• AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)
• Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts
• AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly
• Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue
• Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate
• Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama
• MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness
• LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation
• Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo

Benefits

• 100% remote work
• Flexible Time Off
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture 

The Mission

We are seeking an exceptional Machine Learning Architect to join our growing Cloud Native Applications team. The right candidate is someone who has deep expertise in ML system design and is passionate about working with our customers, partners, and colleagues to drive innovation forward. Your mission will be to work alongside Caylent’s Engineers, Engineering Managers, and Project Managers to deliver AWS solutions across our diverse and forward-thinking customer base. You’ll work with the latest technologies and support customers looking to bring cutting-edge ideas to market.

Your Assignment

• You will be a mission control specialist, guiding Cayliens and Customers alike through Agile ceremonies like stand-ups, retrospectives, and more.
• You will translate customer requirements and into a workable backlog of tickets for engineers.
• Delegate tickets to a team of engineers in order to complete customer projects.
• Lead requirements gathering, backlog grooming, and architecture discussions.
• Apply your understanding of DevOps pipelines, including build automation, branching strategies, CI/CD, Infrastructure as Code, security, monitoring, logging, and alerting.
• Troubleshoot and resolve issues in customer dev, test, and production environments.
• Automate software testing at multiple levels (component, configuration item, subsystem, system) and monitor results.
• Write production quality code, including unit and integration tests. 
• Work with a team to deliver top-quality cloud native applications on AWS for customers.
• Clearly communicate and document your designs, processes, and procedures.
• You will demonstrate a passion to Stay Curious as you mentor peers, tackle new technologies, and learn from our word-class team of engineers.
• Coach and mentor less experienced teammates, coach and be mentored by world class engineers.

Your Qualifications

Core Consulting Qualifications 

Customer Experience 

• Confidently addresses client questions and identifies gaps in requirements with  proposed next steps. 
• Focus on delivering clear, actionable solutions that meet client goals

Collaboration 

• Facilitates alignment across stakeholders or technical priorities and deliverables.
• Navigates differing perspectives with professionalism to ensure shared understanding  and progress. 

Communication 

• Breaks down complex technical concepts for non-technical audiences in a clear, concise  manner. 
• Adapts communication style based on audience and context to improve clarity and decision-making. 

Technical Qualifications

• Experience with IaC tools (CloudFormation, CDK, Terraform).
• Expert level experience in Amazon SageMaker and ML libraries (TensorFlow, MXNet, PyTorch, Scikit-learn).
• Strong understanding of ML concepts: feature engineering, hyperparameter tuning, optimization strategies.
• Familiarity with MLOps tools (MLflow, Neptune, Comet).

Benefits 

• 100% remote work
• Generous holidays and flexible PTO
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

This role may require up to 25% travel, depending on business needs.

About us

Nu was born in 2013 with the mission to fight complexity to empower people in their daily lives by reinventing financial services. We are one of the world’s largest digital banking platforms, serving millions of customers across Brazil, Mexico, and Colombia. For more information, visit our institutional page https://international.nubank.com.br/careers/

Data Science at Nubank

Data Science is essential to every part of the business at Nubank, since Day 1. Most areas of the company use data science to some extent. Data Scientists are responsible for translating business needs into models and other types of data-driven solutions.

As a Data Scientist, you’re expected to

• Interact with other team members to understand how to best apply data science and artificial intelligence to Nubank’s problems;
• Investigate problems and discover insights through exploratory data analysis and other quantitative methods;
• Have great numerical and analytical literacy, including knowledge of statistics and linear algebra as needed;
• Work on multiple stages of the ML model lifecycle–from ideation to training and steady-state operation;
• Work across multiple types of models, algorithms and learning paradigms;
• Communicate technical points, decisions and tradeoffs to peers and stakeholders;
  
  

Our ideal candidate

• Has experience working with ML models in a production environment;
• Understands possible pitfalls and tradeoffs when applying ML to real-word, sometimes messy problems;
• Is familiar with the technical aspects of learning theory and algorithms and is able to discuss details if needed;
• Has experience working with the Python scientific stack (Numpy, Pandas, Scipy, Sklearn, Boosted Tree Libraries, etc);
• Has experience working with Tensor/Deep Learning libraries such as Tensorflow and Pytorch;
• Is able to connect analytical outcomes to business priorities and advise stakeholders on the best course of action;
• Has good communication skills and is able to convey technical topics and decision points to diverse audiences;
• Has experience writing general-purpose code using modern tools;
• Understands the implications of causality when applying ML to problems;
• Is up-to-date with recent developments in the area as a whole and is able to identify opportunities for improvement and innovation;
  
  

Our Benefits

• Chance of earning equity at Nubank
• Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)
• Public Transportation Commuting Benefit (Vale-Transporte)
• NuCare – Psychological, Financial and Legal Assistance Program
• Life Insurance
• Medical Plan
• Dental Plan
• NuLanguage – Language Course Program
• Nucleo - Our learning platform of courses
• Extended Parental Leave
• Daycare Allowance
• Parental Consultancy
• Work-from-home Allowance
• Gym Partnerships
• 30 days of paid vacation
  
  

Work Model for this Role

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

A pessoa nessa posição é responsável por garantir a coleta, processamento, armazenamento e disponibilidade de dados de alta qualidade para impulsionar modelos de IA e fornecer insights acionáveis para clientes e stakeholders.

Trata-se de um papel de liderança técnica, que combina a execução de projetos complexos de ciência de dados com a mentoria do time e a coordenação de esforços de Pesquisa e Desenvolvimento (P&D) para promover a inovação.

Esse propósito se traduz, na prática, nos seguintes Jobs to Be Done:

Liderar a estratégia e execução de Ciência de Dados

• Liderar equipes em projetos complexos, orientando e mentorando membros do time;
• Propor e implementar estratégias para melhorar processos e práticas internas de ciência de dados;
• Decidir quando um modelo está pronto para produção e definir como monitorá-lo.

Desenvolver modelos de IA de ponta a ponta

• Desenvolver modelos utilizando técnicas de IA (estatística, machine learning e deep learning), desde a coleta e tratamento até a engenharia de features;
• Implementar algoritmos de aprendizado supervisionado e não supervisionado, otimizando-os para performance e eficiência;
• Encapsular modelos em APIs robustas, fáceis de usar e bem documentadas.

Transformar grandes volumes de dados em valor

• Coletar, processar e analisar grandes volumes de dados estruturados e não estruturados para identificar padrões e tendências;
• Criar visualizações que elucidem as decisões tomadas e a qualidade dos modelos desenvolvidos.

Atuar como ponte entre tecnologia e negócio

• Facilitar a comunicação entre diferentes departamentos e alavancar o potencial de equipes multidisciplinares;
• Interface direta com clientes e parceiros para levantamento de requisitos técnicos, especificação de arquitetura de modelos e apresentação de resultados.

Sustentar a cultura de inovação da Kunumi

• Manter-se atualizado com as últimas tendências e avanços em ciência de dados e IA;
• Atuar de forma colaborativa e alinhada ao "jeito Kunumi": autonomia, responsabilidade compartilhada e diálogo aberto.

O que é esperado de você

• Liderança Técnica: Experiência sólida liderando projetos e equipes técnicas.
• Domínio de Programação: Proficiência em Python (preferencial), R, SQL e bibliotecas como TensorFlow, PyTorch e Scikit-Learn.
• Ambiente de Desenvolvimento: Experiência de desenvolvimento em ambiente Linux.
• Experiência Prática: Aplicação real de métodos estatísticos e algoritmos de ML para resolver problemas complexos.
• Visualização de Dados: Experiência com ferramentas como Tableau, Matplotlib ou Seaborn.

O que pode ser um diferencial

• Big Data: Familiaridade com tecnologias de armazenamento e processamento como Hadoop, Spark e NoSQL.
• Pesquisa Aplicada: Experiência anterior coordenando esforços de P&D.
• Visão de Produto: Capacidade de traduzir limitações técnicas em soluções de negócio para clientes

Este papel é central na transformação de pesquisa de ponta em aplicações concretas de inteligência artificial, com robustez e escalabilidade. A pessoa nesta função contribuirá para acelerar o ciclo de desenvolvimento científico-tecnológico da Kunumi, encurtando o intervalo entre hipóteses e validações práticas com impacto real.

Esse propósito se traduz, na prática, nos seguintes Jobs to Be Done:

Traduzir hipóteses de pesquisa em artefatos concretos: transformar ideias e hipóteses em construções tangíveis (como modelos, algoritmos, ferramentas ou experimentos) que permitam validar conceitos, gerar aprendizados e produzir impactos no mundo real.

O foco está menos em “prototipar” e mais em viabilizar materializações práticas da pesquisa, conectando o campo teórico a aplicações relevantes, mesmo que em estágios iniciais ou experimentais.

Autorar publicações científicas e fortalecer a presença da Kunumi na comunidade de pesquisa: liderar ou coautorar artigos relevantes, buscar ativamente oportunidades de publicação e colaboração acadêmica, e representar a empresa em fóruns científicos, consolidando a Kunumi como referência em pesquisa aplicada no Sul Global.

Importante: a gente não mede impacto pela quantidade de publicações. O que importa é estar em diálogo com a comunidade científica, acompanhando e contribuindo para o avanço do conhecimento de forma genuína.

Desenvolver infraestrutura para experimentação científica: criar ferramentas, ambientes e pipelines que facilitem a condução de experimentos, a análise de dados e a replicabilidade dos resultados.

Otimizar desempenho e eficiência de modelos: ajustar arquiteturas e fluxos computacionais para garantir que os modelos de IA sejam treinados e executados com o melhor equilíbrio entre custo computacional e performance.

Conectar teoria e aplicação: apoiar os times de pesquisa na adaptação de ideias teóricas para contextos práticos, considerando limitações reais de dados, hardware e integração com sistemas existentes.

Atuar em colaboração com múltiplas frentes da Kunumi: interagir com áreas técnicas e não técnicas, contribuindo para que o conhecimento gerado em pesquisa se traduza em soluções aplicáveis para produtos, negócios ou iniciativas estratégicas.

Promover a melhoria contínua da base técnica da Kunumi: propor soluções mais elegantes, compartilhar boas práticas e apoiar a evolução sustentável da infraestrutura usada nos labs.

O que é esperado de você

Experiência sólida com programação em Python e frameworks como PyTorch ou TensorFlow

Conhecimento aprofundado em matemática aplicada (álgebra linear, cálculo, estatística)

Domínio de algoritmos de aprendizado de máquina e deep learning

Experiência com sistemas distribuídos e pipelines de dados em larga escala

Familiaridade com versionamento (Git), testes de código e práticas modernas de engenharia de software

Capacidade de trabalhar em colaboração com pesquisadores e times multidisciplinares

Experiência com escrita de artigos científicos publicados em anais de congressos e periódicos reconhecidos na área da computação

O que pode ser um diferencial

Experiência prévia em pesquisa aplicada em IA, machine learning ou áreas correlatas

Histórico de publicações em eventos da computação Qualis A1

Contribuições relevantes em projetos open source ou publicações técnicas

Vivência com otimização de modelos para ambientes de produção

Interesse em atuar na interseção entre ciência e impacto social

The Opportunity

This is a senior technical client leadership role that blends deep hands-on ML expertise with strategic advisory and consulting skills. You will be the most experienced ML voice across a diverse and expanding book of customer engagements — from early-stage companies bringing ambitious ML ideas to market, to established enterprises modernizing how they build and operate AI systems on AWS.

You will shape strategy, influence architecture, and leave every team you touch better than you found it. You bring the scientific depth to design and evaluate models rigorously, the engineering depth to architect production ML systems at scale, and the consulting instincts to translate both into business value for customers.

If you have led the hard conversations, shaped the architecture decisions that mattered, and built the things others benchmark against — and you are looking to do that across a growing portfolio of varied and interesting customers — this is the role for you.

What You'll Do

• Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness — producing recommendations that drive executive decision-making and earn Caylent the next engagement.
• Partner with sales and solutions teams through the proposal and scoping phase, contributing the technical depth needed to shape well-grounded statements of work.
• Serve as the senior technical authority on client engagements — possibly across multiple projects simultaneously — providing architectural guidance, ensuring technical quality from your project team members, and getting hands-on when the engagement demands it, without owning day-to-day implementation responsibilities.
• Own or orchestrate high-quality POCs that give customers confidence before committing to a larger initiative.
• Advise customers on ML operations standards and architecture — covering MLOps pipeline design, model lifecycle management, LLMOps patterns, and production monitoring frameworks — translating operational complexity into decisions and guardrails their teams can own and sustain.
• Shape how Caylent wins its most technically complex opportunities — contributing the architectural thinking and credibility that turns prospects into customers.
• Strengthen the ML practice from the inside — through peer guidance, technical interviews, and contributions to accelerators, reference architectures, and thought leadership content.

What You Bring

The non-negotiables

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether traditional ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards and decisions that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization (quantization, vLLM), and distributed training (DeepSpeed, FSDP) — combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS. Technical authority to prescribe the right approach and set architectural standards that teams can execute against.
• Proven ability to operate independently in complex customer environments — navigating ambiguity, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Strong differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
• Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production — spanning automated incident detection, triage, and remediation — with the ability to integrate across observability platforms and translate AI operations outcomes into measurable business value.

Technical Stack

Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it.

ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI

AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)

Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts

AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly

Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue

Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate

Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama

MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness

LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation

Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo

Benefits

Note: This isn’t an active role right now, but we’re building a community of great talent for future opportunities at Caylent. If your background aligns with what we’re looking for, our team may reach out to learn more about you and explore potential future fits.

The Mission

At Caylent, a Senior Machine Learning Engineer works as an integral part of a cross-functional delivery team to design and document machine learning solutions on the AWS cloud for our customers.  We are looking for someone that has a strong understanding of the various model types and tools, and can help our customers connect their business goals with the details of feature design, model training and inference.  You will develop solutions designed by an architect.

You will participate in daily standup meetings with your team and bi-weekly agile ceremonies with the customer.  Your manager will have a weekly 1:1 with you to help guide you in your career and make the most of your time at Caylent.

Your Assignments

• Work with a team to deliver machine learning solutions on AWS for customers
• Participate in and contribute to daily standup meetings
• Develop and implement ML models, MLOps, and analytics
• Big data processing and preparation of training data for models

Your Qualifications

• Strong experience in building ML models for real world applications
• Strong experience in at least one of these:
• AWS ML Services/SageMaker
• ML libraries like Keras, Tensorflow, PyTorch, Scikit-learn
• MLOps tools such as MLflow, Kubeflow, Airflow
• Advanced analytics using time series forecasting and/or inferential statistics
• Strong experience in one or more of these data processing solutions:
• Big data processing platforms like Spark, Hadoop, or streaming platforms
• Data processing and cleansing using Python/Pandas, PySpark, Scala, SQL
• Strong understanding of feature definition, model meta-data, hyperparameter tuning, stochastic gradient descent, deep learning layer types and activation functions
• Experience in visualization using SageMaker, ggplot, matplotlib, or seaborn
• Experience with an IaC tool such as CloudFormation, Amazon CDK or Terraform
• Excellent written and verbal communication skills

Benefits 

• 100% remote work
• Generous holidays and flexible PTO
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

This role may require up to 25% travel, depending on business needs.

AE Studio builds products, platforms, and internal ventures that increase agency for all conscious beings. We combine world-class software engineering, AI expertise, and product design to ship ambitious projects that matter.

ABOUT THE ROLE

As a Data Scientist at AE Studio, you’ll work on a mix of client projects and internal research initiatives. You’ll solve real-world problems, help build meaningful products, and contribute to groundbreaking AI alignment research. Your work will involve building data-driven solutions, leveraging machine learning models, and using your creativity to deliver results that matter.

In addition to client projects, you’ll have the opportunity to propose and pursue high-impact research projects, especially those aligned with our mission of increasing agency. Promising projects could even become prioritized skunkworks initiatives.

We want problem-solvers who are adaptable, collaborative, and excited to push boundaries. If this sounds like you, let’s talk.

ABOUT YOU

You thrive on solving challenging problems and bring a mix of technical expertise, creativity, and humor to your work. You enjoy working independently but also love collaborating with a team that shares your passion for making a difference.

Required Skills and Experience

• Fluency in Python..
• Experience with LLM lifecycle: prompt design/engineering, prompting techniques (RAG, few-shot, CoT, etc.), vector databases, multimodality, fine-tuning, and evaluation.
• Proven data science experience: You’ve been a key contributor to impactful projects and know how to deliver results with real-life outcomes.
• Statistical & causal ML fundamentals: expertise in experimental design, uncertainty quantification, and rigorous model evaluation across tabular, time-series, and foundation-model fine-tuning tasks.
• Deep learning experience: Expertise in building/training NLP or computer vision models with PyTorch, TensorFlow, or JAX.
• Agile & AI-powered development: You run lean Kanban/Scrum cycles and leverage AI-powered tools like Cursor to prototype quickly and ship high-impact solutions.
• Growth mindset: You embrace challenges, value progress over perfection, and constantly seek to improve.
• Self-management: You can work independently, take ownership of projects, and deliver without constant oversight.
• Product and UX understanding: You care about delivering a seamless, user-friendly experience.
• Effective communication in English: Clear, concise, and maybe even witty communication is essential.

Bonus Points for

• Self-managed projects: Ideally something that you helped develop from zero and shipped to real users.
• Startup experience: You thrive in dynamic environments and enjoy taking on new challenges.
• Client relationship management: Experience managing clients and delivering excellent results.
• Passion for AI alignment: You care deeply about humanity’s future and want to help navigate the challenges of advancing AI.

WHAT WE OFFER

• Remote-first culture: Work from anywhere, or join us in one of our offices.
• Flexible schedules: We care about outcomes, not micromanagement.
• Competitive pay: Regular performance-based raises and a diversified employee equity program.
• Opportunities for growth: Work on cutting-edge R&D and propose projects that could become our next skunkworks focus.
• Supportive environment: A team that collaborates, mentors, and shares knowledge.
• Perks: Health insurance, free lunches (at the office), weekly knowledge-sharing talks, and an annual team retreat.

Salary range is from $120K to $220K per year for people based in California. 

Not in the range? That's fine, you can still apply, and our team may fit you in a more senior position that we're not actively hiring right now, but we are growing fast and may have other openings soon.

EQUITY

Working at AE means receiving equity in our client projects and in Skunkworks ventures. The equity is priced at $0 when granted for tax reasons, but AE invests millions into these projects. Equity comes from reduced margins on client work and internal funding. You get a real stake in the things you help build.

The longer you stay, the more value you create and the more equity you receive. If you leave on good terms, you can retain 20% of your equity by staying engaged. If you spin out a Skunkworks venture, you retain 100%.

AE Studio builds products, platforms, and internal ventures that increase agency for all conscious beings. We combine world-class software engineering, AI expertise, and product design to ship ambitious projects that matter.

ABOUT THE ROLE

As a Data Scientist at AE Studio, you’ll work on a mix of client projects and internal research initiatives. You’ll solve real-world problems, help build meaningful products, and contribute to groundbreaking AI alignment research. Your work will involve building data-driven solutions, leveraging machine learning models, and using your creativity to deliver results that matter.

In addition to client projects, you’ll have the opportunity to propose and pursue high-impact research projects, especially those aligned with our mission of increasing agency. Promising projects could even become prioritized skunkworks initiatives.

We want problem-solvers who are adaptable, collaborative, and excited to push boundaries. If this sounds like you, let’s talk.

ABOUT YOU

You thrive on solving challenging problems and bring a mix of technical expertise, creativity, and humor to your work. You enjoy working independently but also love collaborating with a team that shares your passion for making a difference.

Required Skills and Experience

• Fluency in Python..
• Experience with LLM lifecycle: prompt design/engineering, prompting techniques (RAG, few-shot, CoT, etc.), vector databases, multimodality, fine-tuning, and evaluation.
• Proven data science experience: You’ve been a key contributor to impactful projects and know how to deliver results with real-life outcomes.
• Statistical & causal ML fundamentals: expertise in experimental design, uncertainty quantification, and rigorous model evaluation across tabular, time-series, and foundation-model fine-tuning tasks.
• Deep learning experience: Expertise in building/training NLP or computer vision models with PyTorch, TensorFlow, or JAX.
• Agile & AI-powered development: You run lean Kanban/Scrum cycles and leverage AI-powered tools like Cursor to prototype quickly and ship high-impact solutions.
• Growth mindset: You embrace challenges, value progress over perfection, and constantly seek to improve.
• Self-management: You can work independently, take ownership of projects, and deliver without constant oversight.
• Product and UX understanding: You care about delivering a seamless, user-friendly experience.
• Effective communication in English: Clear, concise, and maybe even witty communication is essential.

Bonus Points for

• Self-managed projects: Ideally something that you helped develop from zero and shipped to real users.
• Startup experience: You thrive in dynamic environments and enjoy taking on new challenges.
• Client relationship management: Experience managing clients and delivering excellent results.
• Passion for AI alignment: You care deeply about humanity’s future and want to help navigate the challenges of advancing AI.

WHAT WE OFFER

• Remote-first culture: Work from anywhere, or join us in one of our offices.
• Flexible schedules: We care about outcomes, not micromanagement.
• Competitive pay: Regular performance-based raises and a diversified employee equity program.
• Opportunities for growth: Work on cutting-edge R&D and propose projects that could become our next skunkworks focus.
• Supportive environment: A team that collaborates, mentors, and shares knowledge.
• Perks: Weekly knowledge-sharing talks, an annual team retreat, and much more.

EQUITY

Working at AE means receiving equity in our client projects and in Skunkworks ventures. The equity is priced at $0 when granted for tax reasons, but AE invests millions into these projects. Equity comes from reduced margins on client work and internal funding. You get a real stake in the things you help build.

The longer you stay, the more value you create and the more equity you receive. If you leave on good terms, you can retain 20% of your equity by staying engaged. If you spin out a Skunkworks venture, you retain 100%.

About us

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

About AB InBev Growth Group

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.

About the Role

The Senior Data Scientist will be responsible for developing and leading solutions at the intersection of Computer Vision, Computer Graphics, and Multimodal Models. This role involves tackling strategic challenges, combining traditional computer vision techniques with cutting-edge scene reconstruction and generative AI, focusing on detection, classification, visual similarity, synthetic data generation, and multimodal interpretation. You will act as a technical reference within the team, guiding architectural decisions and defining the best approaches for complex visual problems. You will work in a highly collaborative, global environment, transforming visual data into robust and scalable models, ensuring automation, efficiency, and high performance across implemented pipelines. We are looking for someone with an analytical mindset, curiosity, a strong technical foundation in the mentioned areas, excellent communication skills, a collaborative spirit, and a passion for innovation and continuous learning.

What you'll do

• Define and lead the technical approach for object detection solutions (e.g., YOLO, RT-DETR, and similar architectures), applying them to real-world problems with an emphasis on performance, accuracy, and scalability.
• Define and develop image classification strategies, with experience in architectures such as EfficientNet, MobileNet, and ResNet, focusing on production readiness.
• Lead the development of Siamese networks for verification and visual similarity tasks, applying fine-tuning and optimization strategies for image comparison and visual integrity validation.
• Define and apply multimodal generative and instruction-following models such as Qwen-VL, PaliGemma, and BLIP, using fine-tuning techniques like LoRA and QLoRA, focusing on image-text alignment.
• Knowledge of scene reconstruction techniques such as NeRF or Gaussian Splatting, and hands-on experience with frameworks like Nerfstudio or similar.
• Work with large volumes of visual data, coordinating curation, annotation, and qualitative evaluation strategies to ensure diversity and quality in image datasets.
• Generate and use synthetic and 3D data as complementary sources for training and evaluating models, supporting their creation and curation to enhance model generalization and robustness.
• Automate, version, and monitor training, inference, and deployment pipelines using Python, PySpark, MLflow, GitHub Actions, Docker, and CI/CD, running on cloud infrastructure (especially Azure and Databricks).
• Mentor junior and mid-level data scientists, supporting their technical growth and ensuring quality across the team's deliverables.
• Communicate technical results and trade-offs to senior stakeholders, ensuring strategic alignment of developed solutions.
• Collaborate with data engineers, data scientists, PMs, and business stakeholders, ensuring technical alignment and strategic impact of developed solutions.
• Work in an agile, global environment, participating in Scrum/Kanban ceremonies, technical reviews, and continuous delivery and learning cycles.

What you'll need

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related fields. A master's or PhD is strongly preferred.
• General and foundational knowledge in data science and statistics, including data analysis, modeling, and validation techniques.
• Strong background in Deep Learning, including mastery of Transformer architectures and advanced algorithms applied to real-world problems.
• Proven experience building and deploying models for object detection, image classification, visual information extraction, and multimodal interpretation.
• Proficiency in TensorFlow or PyTorch, with experience in model development and deployment.
• Experience with cloud computing, particularly Azure, Databricks, and Spark, for large-scale data processing.
• Expertise in Python and PySpark, plus familiarity with data manipulation and modeling libraries and frameworks.
• Knowledge of version control and collaboration tools such as GitHub, and experience with CI/CD pipelines for data and model projects.
• Understanding of automation workflows, model monitoring, and best practices for maintaining performance and robustness.
• Experience leading technical projects and guiding architectural decisions in data science or machine learning teams.
• Experience deploying models to production environments, including optimization and monitoring.
• Advanced English to operate in a global environment.
• Strong interpersonal skills, communication, teamwork, organization, autonomy, and focus on delivering high-quality results.

What We Offer:

• Performance based bonus*
• Attendance Bonus* 
• Private pension plan
• Meal Allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance
• Medicines discounts
• WellHub partnership
• Childcare subsidies
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship*
• School materials assurance
• Language and training platforms
• Transport allowance

*Rules applied

Equal Opportunity & Affirmative Action:

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.

The following fields are optional, but anticipate the information for your registration*.

Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company. 

For more information: www.abinbev.com 

A Capco é uma consultoria global de tecnologia e negócios, focada no setor de serviços financeiros e energia, somos apaixonados por ajudar nossos clientes, temos 40 escritórios nas Américas, Europa e Ásia-Pacífico! Se criatividade e inovação são sua paixão, a Capco é ideal para você. Vamos te apoiar e ajudar a acelerar sua carreira!

Estamos em busca de um(a) Machine Learning Engineer com foco em NLP e processamento de documentos para atuar no desenvolvimento de soluções inteligentes voltadas à automação e análise de dados, especialmente em contextos financeiros. O(a) profissional será responsável por construir pipelines de dados, desenvolver modelos de machine learning e implementar soluções escaláveis em ambiente cloud.

O que você fará:

• Desenvolver soluções de machine learning e NLP para extração e processamento de informações de documentos
• Trabalhar com OCR, parsing de PDFs e extração de dados de Excel
• Implementar algoritmos de fuzzy matching (ex: Levenshtein, record linkage)
• Construir e manter pipelines de dados e processos de ETL
• Desenvolver, treinar e validar modelos de machine learning (supervisionados e não supervisionados)
• Realizar deploy de modelos utilizando APIs REST, Docker e serviços Azure
• Integrar soluções com sistemas externos e aplicações corporativas
• Colaborar com times multidisciplinares para entrega de soluções escaláveis

O que você precisa ter:

• Forte experiência em Python (Pandas, NumPy, scikit-learn)
• Experiência com bibliotecas de NLP (spaCy, NLTK ou Transformers)
• Conhecimento em processamento de documentos (OCR, PDFs, Excel)
• Experiência com deploy de modelos (APIs REST, Docker, Azure ML)
• Entendimento de técnicas de machine learning supervisionado e não supervisionado
• Experiência com pipelines de dados e ETL
• 2 a 5 anos de experiência em Machine Learning, com foco em NLP ou dados financeiros
• Inglês avançado/fluente

Diferenciais

• Experiência no setor financeiro/fintech (reconciliação, contabilidade)
• Conhecimento em Azure Machine Learning e Cognitive Services
• Experiência com frameworks de deep learning (TensorFlow, PyTorch)
• Experiência com LLMs para entendimento de documentos (OpenAI, Azure OpenAI)

POR QUE CAPCO:

Na Capco promovemos uma cultura inclusiva. Valorizamos a diversidade em todas as suas expressões. 
Pensamos, em conjunto, sempre em ações diversas de inclusão e de responsabilidade social através de comitês internos geridos pela nossa comunidade interna, como os grupo de Mulheres, Pessoas Com Deficiência, Pessoas Negras, LGBTQIAPN+, Parentalidade, Gerações, entre outros. Nossas oportunidades são trabalhadas para todos(as)!
#LifeAtCapco

PRÓXIMOS PASSOS:

• Se você está ansioso(a) para progredir sua carreira conosco, candidate-se e aguarde o contato de um de nossos recrutadores!

About us

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

About AB InBev Growth Group

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.

What you'll do:

• Drive BEES AI’s mission, vision, and ML strategy into concrete technical roadmaps and align architecture and standards with long-term goals.
• Own technical end-to-end delivery of ML pipelines—from architecture and data/feature pipelines through model development, deployment, and production scaling—in partnership with data, platform, and product teams.
• Design and standardize the ML model development framework (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.
• Build and implement observability across the model lifecycle (training and production) via monitoring, logging, and alerting for performance, quality, and drift.
• Ensure optimized deployments that minimize cost and maximize quality (e.g., scaling, resource allocation, inference optimization) while meeting SLAs.

What you'll need:

• Bachelor's degree in computer science, engineering, mathematics, or any quantitative field. A master's degree is a plus.
• Relevant experience with ML platform architecture (e.g., feature stores, model registries, training and inference pipelines).
• Systems thinking (scalability, reliability, latency, cost)
• Software design patterns for building maintainable, production-ready ML systems.
• Python, PySpark, and SQL. Java, Rust, and NodeJS are a plus
• Kubernetes, Databricks, Terraform, Azure Devops (Git), Azure Cloud, ML Frameworks (BentoML, Kedro, Seldon, KServe, Triton Inference Server, etc.) and ML Libraries (Scikit Learn, Pytorch, Tensor Flow, ONNX, etc.)

More about you:

• You´re motivated by building up world-class solutions that impact millions of users all over the world;
• You´re creative when solving problems and is continuously seeking improvements for processes and solutions;
• You can communicate clearly with your team and the company to identify issues and demands;
• You have the autonomy to recognize the current and new priorities, evaluating the impact of their outcomes in the final result.

What We Offer:

• Performance based bonus*
• Attendance Bonus* 
• Private pension plan
• Meal Allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance
• Medicines discounts
• WellHub partnership
• Childcare subsidies
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship*
• School materials assurance
• Language and training platforms
• Transport allowance

*Rules applied

Equal Opportunity & Affirmative Action:

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.

The following fields are optional, but anticipate the information for your registration*.

Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company. 

For more information: www.abinbev.com 

Your wellbeing, our mission. Join a company shaping a healthier world.

GET TO KNOW US

At Wellhub we're revolutionizing workplace wellness. Our platform connects employees worldwide to the best partners for fitness, mindfulness, therapy, nutrition, and sleep—all in one simple subscription. Headquartered in NYC with team members in Europe, North America and South America, we’re on a mission to make every company a wellness company.

We believe work should be fulfilling, inspiring, and balanced. Here, you’ll find a team that values wellbeing, collaboration, and different perspectives, where passion and creativity push boundaries to create real impact. Your contributions will help shape a healthier, more balanced world for you and millions of people globally. 

Join us in redefining the future of wellbeing!

THE OPPORTUNITY

We are hiring a Manager Data Scientist to our Engagement & Platform team in Brazil! This is a Remote – Brazil position, meaning you can work from anywhere within the country. Please note that this role is only open to candidates in Brazil. 

As a Data Science Manager, you will be responsible for shaping the ML and experimentation strategy behind our engagement system, including ML models (e.g., contextual bandits, reinforcement learning, classification, etc) and AI agents that produce dynamic, personalized nudges to the Wellhub AI users. You will work closely with Product, Engineering, and Data Scientists to design, build, and evaluate systems that operate at scale. This role also includes people management, mentoring, and raising the bar on scientific rigor across the team.

YOUR IMPACT

• Machine Learning Systems: Design and develop ML models for outreach personalization, including contextual bandits, reinforcement learning, and classification models.
• AI Agents: Design and develop AI agents that generate dynamic, personalized nudge content and conversational interventions.
• A/B Testing & Experimentation: Design and analyze large-scale A/B tests and experimentation frameworks to evaluate ML models, data-driven signals, nudges, and content strategies. Define success metrics and ensure statistically sound experimentation practices.
• Data Pipelines & Analytics: Collaborate on the design of data pipelines and event schemas that feed the product, ensuring high-quality data for real-time decisioning and offline analysis.
• Evaluation & Monitoring: Define and implement robust monitoring frameworks for the product, including short-term engagement metrics, longer-term retention, opt-outs, and intervention fatigue.
• Leadership & People Management: Lead, mentor, and grow the data scientists of the team. Set technical direction, ensure high-quality delivery, and foster strong collaboration with Product and Engineering stakeholders.
• Live the mission: inspire and empower others by genuinely caring for your own wellbeing and your colleagues. Bring wellbeing to the forefront of work, and create a supportive environment where everyone feels comfortable taking care of themselves, taking time off, and finding work-life wellness.

WHO YOU ARE

• BSc., Master’s or PhD degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field;
• At least 8 years of professional experience in Data Science, Machine Learning, or Applied AI roles;
• At least 3 years of experience in people management, leading and developing data science teams;
• Strong proficiency in Python and experience with ML frameworks (e.g., Vowpal Wabbit, PyTorch, or similar);
• Strong foundation in experimentation, A/B testing, and large-scale experimentation design and analysis;
• Experience working with Generative AI systems in production environments;
• Solid problem-solving skills and ability to translate product goals into measurable, data-driven solutions;
• Strong communication skills and ability to work cross-functionally with Product and Engineering teams.

Preferred Qualifications:

• Experience applying ML techniques such as contextual bandits, reinforcement learning, and classification models in production systems.
• Familiarity with real-time or near-real-time decisioning systems for personalization.
• Experience designing data pipelines or collaborating closely with data engineering teams.
• Background in consumer products or digital health is a plus.

We recognize that individuals approach job applications differently. We strongly encourage all aspiring applicants to go for it, even if they don't match the job description 100%. We welcome your application and will be delighted to explore if you could be a great fit for our team. For this specific role, please note that prior experience in Machine Learning and people management are mandatory requirements.

WHAT WE OFFER YOU

With thoughtful benefits, emotional wellbeing resources, and a culture that empowers you to take ownership of your role and your wellbeing, we create an environment where you can thrive in all dimensions of your life. 

Our flexible benefits program allows you to customize some of the benefits, according to your needs!

Our benefits include:

WELLHUB: Free Gold+ membership with access to onsite gyms and studios, digital fitness programs, and online wellness resources for meditation, nutrition, mental wellbeing support, and more! Add up to three family members to your plan, ensuring access to wellness for those who matter most to you.

WELLZ: A complete emotional wellbeing program with a unique approach. It offers personalized journeys that combine individual therapy sessions (52 per year) and on-demand content. 

HEALTHCARE: Health, dental, and life insurance. 

FLEXIBLE WORK: As a Flexible First company, we offer hybrid and remote options to give you the freedom to work in a way that suits you. The model for this specific role can be discussed with your recruiter and hiring manager. When you join, use our home office reimbursement to set up your home office. 

FLEXIBLE SCHEDULE: Flexibility for us isn’t just about where we work—it also means being able to shape how and when we get things done. Together with their leaders, employees define schedules that align with their time zones, team needs, and personal routines.

PAID TIME OFF: It’s important to take time away from work to recharge.Employees receive vacations after 6 months and additional 3 days off per year + 1 day off for each year of tenure (up to 5 additional days) + an extra holiday for your birthday!

PAID PARENTAL LEAVE: Welcoming a new child is one of the most special moments in your life. Take the time to be present and enjoy your growing family. We offer 100% paid parental leave to all new parents. Parents giving birth are eligible for an extended leave and a ramp-back period to return part-time while they get settled.  

CAREER GROWTH: Access world-class platforms, participate in interactive sessions,  build your personalized development roadmap, and explore internal opportunities. We focus on continuous learning and feedback to support your journey toward personal and professional success.

CULTURE: You’ll join a team of passionate people who come together to break boundaries, support each other, and create a meaningful impact in workplace wellness. We win together, building trust through open communication and a culture where every perspective matters. Learn more about our shared culture and values here. 

And to get a glimpse of life at Wellhub… Follow us on Instagram @lifeatwellhub and LinkedIn!

Diversity, Equity, and Belonging at Wellhub

We aim to create a collaborative, supportive, and inclusive space where everyone knows they belong. 

Wellhub is committed to creating a diverse work environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, gender identity or expression, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law.

Our commitment to inclusion also extends to how we recognize and reward our people. We’re proud to be Syndio Fair Pay Certified, reflecting our ongoing dedication to equitable and fair pay practices across our global team. Read more about it here.

Questions on how we treat your personal data? See our Aviso de Privacidade para Candidatos.

#LI-REMOTE

Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity.

Xometry is looking for a Machine Learning Engineer II who is excited about advancing machine learning capabilities and bringing models into production at scale. In this role, you’ll design, deploy, and maintain robust statistical and machine learning models, working closely with data scientists to translate research and experimentation into reliable, high-impact systems. You’ll apply strong data intuition and engineering judgment to improve model performance, reliability, and observability, while building predictive models that support pricing, cost estimation, and sourcing recommendations.

What You'll Do:

• Design, build, and optimize machine learning models to enhance Xometry’s platform and business operations.
• Analyze large datasets to extract meaningful patterns and insights.
• Collaborate with cross-functional teams to integrate machine learning models into production systems.
• Learn and apply best practices in model evaluation, performance tuning, and deployment.
• Influence technical direction by identifying opportunities to improve modeling approaches, data quality, and system architecture.
• Work across teams to ensure machine learning solutions are explainable, maintainable, and aligned with business goals.
• Help bridge the gap between research and production, ensuring models perform just as well in the real world as they do in notebooks.
• Gain exposure to cutting-edge machine learning frameworks, tools, and techniques used in the manufacturing industry.

Qualifications:

• A bachelor’s degree is required, but an advanced degree (M.S. or PhD) in computer science, machine learning, AI, or a related field is highly preferred.
• Experience deploying and maintaining machine learning models in production environments.
• 4+ years of experience in machine learning, focusing on data engineering and/or data science.
• Proficient in Python, including key libraries such as PyTorch, TensorFlow, pandas, and numpy.
• Strong background in probability, statistics, and optimization techniques relevant to generative modeling.
• Familiarity with cloud computing resources and tools for model training and deployment (e.g., AWS SageMaker).
• Familiar with software engineering principles, including version control, reproducibility, and continuous integration.
• Experience in the manufacturing, supply chain, or similar industries is a plus.

#LI-Remote

Xometry is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.

For US based roles: Xometry participates in E-Verify and after a job offer is accepted, will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.

Xometry is seeking a Senior Data Scientist to join our Generative AI team. The candidate will focus on training and fine-tuning Visual Language Models (VLMs) for multimodal document understanding. The ideal candidate will leverage their expertise in machine learning and computer vision to advance Xometry's capabilities in processing and extracting structured data from complex documents and images. This is a 1-year contract.  

Responsibilities:

• Develop, fine-tune, and evaluate Visual Language Models (VLMs) to enhance document understanding, focusing on multimodal data such as text, images, and technical drawings.
• Design and implement data preparation, cleaning, and augmentation processes tailored to multimodal model training, ensuring high-quality data pipelines for VLMs.
• Leverage transfer learning and pre-trained models to accelerate model development and optimize performance on Xometry’s specific data.
• Use cloud resources (e.g., Amazon Web Services) to scale training and fine-tuning processes for VLMs efficiently.
• Collaborate with data engineering and machine learning operations (MLOps) teams to deploy VLMs into production and monitor their performance.
• Interpret model outputs and improve model accuracy and robustness by applying data analysis and visualization tools (such as Python, Jupyter Notebooks, and SQL).
• Experiment with and implement state-of-the-art model architectures, continuously optimizing VLM performance in a fast-paced, iterative environment.
• Work within a team-oriented setting, participating in peer reviews, sharing insights, and contributing to an environment of continuous learning and improvement.

Qualifications:

• A bachelor’s degree is required; an advanced degree (M.S. or PhD) in computer science, data science, machine learning, or a related field is highly preferred.
• 5+ years of experience in data science and machine learning, with expertise in Visual Language Models or multimodal machine learning.
• Strong experience with machine learning libraries and frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Proficiency in Python, including libraries like pandas, numpy, and scikit-learn.
• Solid understanding of deep learning techniques and experience with transfer learning, fine-tuning, and model evaluation.
• Experience with cloud platforms (e.g., AWS SageMaker) for model training and deployment.
• Familiarity with data processing and visualization tools (SQL, Jupyter Notebooks, Looker, etc.) and basic database knowledge (e.g., Snowflake, MongoDB).
• Excellent analytical and problem-solving skills, with a strong ability to work in an environment that values teamwork, innovation, and continuous learning.
• Familiarity with computer vision tasks and frameworks, as well as experience with multimodal data, is a plus.

#LI-Remote

Xometry is seeking a Senior Machine Learning Engineer to join our growing organization. The right person will help move our machine learning capabilities to the next level. You’ll be working in an organization where AI is the enabler and center of its core business strategy. You want to be part of a small, intensely skilled team, who feel total ownership of their work, and can’t imagine a day without learning & coding.  You will play a crucial role in the Xometry platform and everything you do will matter.

Responsibilities:

• At Xometry, you will play a crucial role in exploring new machine learning opportunities, researching and performing proof of concepts, and bringing new machine learning and AI solutions into Xometry’s platform
• Develop and implement machine learning models that improve Xometry’s ability to predict cost, price, and sourcing options for our customers and suppliers.
• You will be responsible for leading the evaluation of emerging technologies, identifying areas for improvement, and developing new features while ensuring the reliability and scalability of Xometry’s platform
• Lead the exploration of emerging AI and machine learning technologies and develop proof-of-concepts to assess their potential impact on Xometry’s platform
• Collaborate with cross-functional teams to gather requirements, prioritize features, and define technical solutions based on the latest innovations
• Monitor machine learning models and AI performance and troubleshoot issues as they arise
• Contribute to the documentation and knowledge base to help other teams understand and use Xometry’s AI effectively

Qualifications:

• Experience with state of the art ML modeling techniques and approaches like transformers, self supervised pre-training, generative modeling, LLMs, etc
• Experience with large scale data processing (e.g., Hands-on experience training and applying models at scale using deep learning frameworks like PyTorch or Tensorflow)
• Successful candidates in this role need to be able to bridge state of the art approaches to real world impact
• A cloud-native software craftsperson with hands on experience with containers, container orchestration, and distributed systems
• An engineer with a self-driven attitude who can own problems and deliver solutions
• Strong software fundamentals with experience in design patterns, refactoring, OOP, and testing
• A cloud practitioner who values the “shift-left” mentality by building systems with Infrastructure as Code, not console clicking
• Data’s at the heart of our systems, we hope it's yours too. You’re comfortable working with SQL and/or NoSQL, understand database design, and data analysis
• Experience building and consuming CI/CD pipelines
• Someone who knows their way around the command line and has experience working with *nix systems
• 5+ years working experience in the engineering teams that build large-scale ML-driven user-facing products
• 3+ years experience leading cross-team engineering efforts
• Strong execution skills in project management

#LI-Remote

Xometry is seeking a Senior Machine Learning Engineer to join our growing organization. The right person will help move our machine learning capabilities to the next level. You’ll be working in an organization where AI is the enabler and center of its core business strategy. You want to be part of a small, intensely skilled team, who feel total ownership of their work, and can’t imagine a day without learning & coding.  You will play a crucial role in the Xometry platform and everything you do will matter.

Responsibilities:

• At Xometry, you will play a crucial role in exploring new machine learning opportunities, researching and performing proof of concepts, and bringing new machine learning and AI solutions into Xometry’s platform
• Develop and implement machine learning models that improve Xometry’s ability to predict cost, price, and sourcing options for our customers and suppliers.
• You will be responsible for leading the evaluation of emerging technologies, identifying areas for improvement, and developing new features while ensuring the reliability and scalability of Xometry’s platform
• Lead the exploration of emerging AI and machine learning technologies and develop proof-of-concepts to assess their potential impact on Xometry’s platform
• Collaborate with cross-functional teams to gather requirements, prioritize features, and define technical solutions based on the latest innovations
• Monitor machine learning models and AI performance and troubleshoot issues as they arise
• Contribute to the documentation and knowledge base to help other teams understand and use Xometry’s AI effectively

Qualifications:

• Experience with state of the art ML modeling techniques and approaches like transformers, self supervised pre-training, generative modeling, LLMs, etc
• Experience with large scale data processing (e.g., Hands-on experience training and applying models at scale using deep learning frameworks like PyTorch or Tensorflow)
• Successful candidates in this role need to be able to bridge state of the art approaches to real world impact
• A cloud-native software craftsperson with hands on experience with containers, container orchestration, and distributed systems
• An engineer with a self-driven attitude who can own problems and deliver solutions
• Strong software fundamentals with experience in design patterns, refactoring, OOP, and testing
• A cloud practitioner who values the “shift-left” mentality by building systems with Infrastructure as Code, not console clicking
• Data’s at the heart of our systems, we hope it's yours too. You’re comfortable working with SQL and/or NoSQL, understand database design, and data analysis
• Experience building and consuming CI/CD pipelines
• Someone who knows their way around the command line and has experience working with *nix systems
• 5+ years working experience in the engineering teams that build large-scale ML-driven user-facing products
• 3+ years experience leading cross-team engineering efforts
• Strong execution skills in project management

#LI-Remote

About Us

Nu was born in 2013 with the mission to fight complexity to empower people in their daily lives by reinventing financial services. We are one of the world’s largest digital banking platforms, serving millions of customers across Brazil, Mexico, and Colombia.

About the role

At AI Core, we are scaling the impact of our AI initiatives to become the primary driver of Nubank’s most critical decision systems. We are seeking an Lead Machine Learning Engineer (Applied AI Scientist) to lead high-impact research projects that bridge the gap between state-of-the-art AI and production-grade financial systems. You will be responsible for solving complex, ambiguous problems using Deep Learning and Foundation Models, ensuring our architectures are scalable, efficient, and driving measurable business results.

As an Applied AI Scientist (MLE), you’re expected to:

1. Research Execution & Technical Leadership (Complexity & Autonomy)

• Lead and execute complex applied research initiatives independently, focusing on building and optimizing architectures (e.g., Transformers, GNNs) that can be deployed across critical use cases like Credit, RecSys, GenAI, and real-time inference.
• Address difficult and ambiguous modeling problems that require coordination across various stakeholders (Data, Infra, Product), delivering innovative solutions with a clear focus on medium-term impact.
• Bridge the gap between research and production by designing architectures that respect MLOps constraints, ensuring models are optimized for latency, interpretability, and cost-efficiency.

1. Strategic Impact & Collaboration (Impact)

• Develop and deliver innovative solutions that address project-level challenges, focusing on pushing the latest platform and AI research improvements into downstream production models.
• Actively participate in cross-functional collaborations, ensuring that research outputs are seamlessly integrated into Nubank's decision-making engines.
• Establish technical standards within the AI Core team for experimentation, model evaluation, and code quality, inspiring peers to raise their performance.

1. Mentorship & Function Contribution (Function Contribution)

• Serve as a technical mentor for senior engineers and researchers, providing guidance on deep learning fundamentals, problem formulation, and research methodology.
• Actively contribute to the function's growth by participating in mandatory activities like hiring (interview panels) and leading internal task forces to improve our ML lifecycle.
• Contribute to thought leadership by participating in research collaborations or internal papers that align with Nubank’s strategic goals.

What are we looking for?

• Professional Experience: 5-7+ years in applied AI/ML, with a proven track record of delivering research-driven systems into production environments
• Technical Mastery:
  • Deep expertise in Deep Learning architectures (Transformers, Multimodal, or GNNs).
  • Strong coding skills in Python and proficiency with frameworks like PyTorch, JAX, or TensorFlow.
  • Solid understanding of MLOps and the constraints of deploying models at scale.
• Problem Solving: Sophisticated skills in ML problem formulation and the ability to navigate uncertainty when data is messy or unavailable.
• Communication: Ability to communicate complex technical concepts to both technical peers and cross-functional stakeholders, ensuring alignment and buy-in.
• Analytical Capacity: Experience with large-scale experimentation and A/B testing to validate research hypotheses.

Our Benefits

• Chance of earning equity at Nubank
• Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)
• Public Transportation Commuting Benefit (Vale-Transporte)
• NuCare – Psychological, Financial and Legal Assistance Program
• Life Insurance
• Medical Plan
• Dental Plan
• NuLanguage – Language Course Program
• Nucleo - Our learning platform of courses
• Extended Parental Leave
• Daycare Allowance
• Parental Consultancy
• Work-from-home Allowance
• Gym Partnerships
• 30 days of paid vacation
• Relocation Assistance Package, if applicable

Work Model for this Role

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

Principais Atividades:

• Desenvolver e manter APIs e microservices com tecnologias modernas.
• Trabalhar com arquitetura de software, garantindo escalabilidade e segurança.
• Atuar com dados e analytics, utilizando ferramentas como SQL, Power BI, Python, Spark e plataformas de Big Data.
• Criar e otimizar soluções em Cloud Computing (AWS, Azure, GCP), com práticas DevOps e CI/CD.
• Participar de times ágeis (Scrum, Kanban) e colaborar em projetos inovadores para grandes empresas

Requisitos:

• 5-6 anos de experiência sólida experiência comprovada em desenvolvimento de software com foco em inteligência artificial;
• Graduação em Ciência da Computação, Engenharia, Matemática ou área relacionada
• Proficiência em Python e experiência com bibliotecas como TensorFlow, PyTorch, Scikit-learn, etc;
• Conhecimento em LLM (Linguagem de Modelagem Lógico) e RAG (Rede Adaptativa Generativa) é altamente desejável;
• Experiência em trabalhar com serviços em nuvem, especialmente AWS;
• Excelentes habilidades de resolução de problemas e capacidade de trabalhar de forma independente e em equipe;
  Inglês intermediário/avançado (possivel atuação com time internacional).

Por que a Capco:

Na Capco, promovemos uma cultura inclusiva. Valorizamos a diversidade em todas as suas expressões. Pensamos, em conjunto, sempre em ações diversas de inclusão e de responsabilidade social através de comitês internos geridos pela nossa comunidade, como os grupo de Mulheres, Pessoas Com Deficiência, Pessoas Negras, LGBTQIAPN+, Parentalidade, Gerações, entre outros. Juntar-se à Capco significa ingressar em uma organização comprometida com um ambiente de trabalho inclusivo onde você é incentivado a #BeYourselfAtWork (Ser Você Mesmo no Trabalho). Celebramos a individualidade e reconhecemos que a diversidade e a inclusão, em todas as formas, são fundamentais para o sucesso. Acreditamos que todos trazem algo diferente, por isso adoraríamos saber o que o torna diferente! #LifeAtCapco
Juntar-se à Capco significa ingressar em uma organização comprometida com um ambiente de trabalho inclusivo onde você é incentivado a #BeYourselfAtWork (Ser Você Mesmo no Trabalho). Celebramos a individualidade e reconhecemos que a diversidade e a inclusão, em todas as formas, são fundamentais para o sucesso. Acreditamos que todos trazem algo diferente, por isso adoraríamos saber o que o torna diferente!

Próximos Passos:

O objetivo da Capco é conduzir um processo flexível e alinhado às necessidades de cada oportunidade e talento.

• Etapa 1: Entrevista comportamental.
• Etapa 2: Entrevista técnica.
• Etapa 3: Conversa com time de Projeto/ Delivery.
• Etapa 4: Conversa com cliente.
• Etapa 5: Proposta ou feedback.

As etapas do nosso processo seletivo descritas são essenciais para garantir uma avaliação completa e assertiva. Contudo, elas podem ser ajustadas dependendo da senioridade do(a) candidato(a), da área de atuação e do formato do projeto.