As a Senior Machine Learning Engineer in Computer Vision, you will design and deliver advanced vision systems that power mission-critical applications for global and Fortune 500 companies. You’ll work across deep learning, large-scale data pipelines, and high-performance infrastructure, owning models end-to-end from experimentation to production deployment.

This role is designed for engineers who think systems-level, understand the real-world constraints of ML at scale, and can turn ambiguous visual problems into high-impact, production-ready solutions. You’ll shape architectures, guide model strategy, and bring modern vision capabilities into enterprise environments where reliability, speed, and accuracy matter.

Functional Responsibilities:

• Develop and fine-tune models for tasks like image classification, object detection, segmentation, and generative modeling using TensorFlow, PyTorch, or Keras.
• Implement techniques such as resizing, normalization, data augmentation, and feature extraction to improve model performance.
• Optimize and deploy computer vision models on cloud platforms (AWS, GCP, Azure), edge devices, and specialized hardware (GPUs, TPUs).
• Use CI/CD, model versioning, and monitoring tools to ensure reliable and scalable deployment of vision models.
• Improve model speed and performance using quantization, pruning, and hardware acceleration techniques.

Qualifications:

• +5 years of hands-on experience developing and deploying machine learning models in production environments. 
• Proven experience writing production-level code, with strong proficiency in Python.
• Strong Python programming skills with proficiency in deep learning frameworks (TensorFlow, PyTorch, or Keras).
• Expertise in designing, training, and fine-tuning models for: Image classification (ResNet, EfficientNet), Object detection (Faster R-CNN, YOLO, SSD) or Image segmentation (U-Net, Mask R-CNN).
• Strong understanding of image preprocessing techniques (resizing, normalization, data augmentation).
• Experience with computer vision libraries such as OpenCV and torchvision.
• Experience with transfer learning and adapting pre-trained models.
• Ability to deploy models on cloud platforms (AWS, GCP, Azure) and specialized hardware (GPUs, TPUs).
• Familiarity with MLOps tools for automating ML pipelines.

We are currently looking for an exceptionally talented Senior Data Scientist - Time Series Specialist to join our team and help build cutting-edge solutions at the intersection of manufacturing and advanced analytics. In this role, you will design and deploy scalable ML systems that detect anomalies, uncover root causes, and transform complex manufacturing and test log data into actionable insights that drive product quality and operational efficiency.

Functional Responsibilities:

• Design and implement ML and statistical models for anomaly detection on large-scale manufacturing test logs and machine outputs.
• Work with high-volume time series and structured machine log data, meeting low-latency requirements in production environments.
• Build both real-time (live line) detection pipelines and post-hoc failure analysis and root cause analysis workflows.
• Translate domain expertise into scalable, production-ready systems, ensuring reliability, observability, and performance.
• Continuously evaluate and apply emerging techniques in machine learning and time series analysis to improve model performance and robustness.

Qualifications:

• 4+ years of experience in Data Science, Machine Learning, or a related field.
• Hands-on experience building anomaly detection models, ideally within manufacturing or industrial environments.
• Strong experience working with high-frequency time series data, including feature engineering for sequential signals.
• Proficiency in Python and SQL, with experience using ML libraries such as TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of machine learning, statistical modeling, and data analysis techniques.
• Proven ability to work with large-scale datasets under low-latency constraints in production or near-production environments.
• Experience translating models into production systems (e.g., basic ML deployment, experiment tracking, or CI/CD workflows) is a plus.
• Strong communication skills in English (B2–C2), with the ability to clearly explain complex technical concepts and collaborate across teams.

Our Program:

The Factored AI Machine Learning & GenAI Residency is a full-time, paid professional residency designed for experienced engineers who want to operate at a higher level of responsibility, complexity, and impact. This residency is not a course or a traditional training program. It is a selective, execution-driven environment where engineers validate and elevate how they build and operate real Machine Learning and Generative AI systems in production.

Residents work within real constraints, alongside peers who meet the same engineering bar, on systems that matter.

Highlights

• A full-time, paid residency with clear professional expectations
• Work on real production systems, not academic exercises or toy projects
• Ownership of system-level decisions across architecture, performance, reliability, and cost
• Continuous technical evaluation through real execution, not exams
• Exposure to the standards used to build and operate AI systems for U.S. companies

This residency is designed to function as a professional validation layer for senior engineers.

Structure & Support

The residency operates as a structured professional track focused on execution.

• Residency Period: 12 to 16 weeks, depending on readiness and scope
• Time Commitment: Full-time (approximately 45 hours per week)
• Environment: Hands-on execution, technical reviews, system-level discussions
• Compensation: Paid, reflecting the professional nature of the residency

Residents are supported with access to infrastructure, peer feedback, and senior technical guidance aligned with Factored’s engineering standards.

Qualifications

We are looking for engineers who already have strong foundations and real production experience.

Required background:

• 4+ years of experience building and deploying Machine Learning systems in production
• Strong Python proficiency, writing production-grade code
• Solid understanding of Deep Learning fundamentals, including model architecture, optimization, and training dynamics
• Experience working with NLP and modern ML frameworks such as PyTorch, TensorFlow, or Hugging Face
• Backend experience building APIs with FastAPI or Flask
• Experience with containerization and cloud deployment (AWS, Azure, or GCP)
• Familiarity with production MLOps practices, including CI/CD, model versioning, monitoring, and infrastructure as code
• Strong written and spoken English, with the ability to communicate technical decisions clearly
• Must be based in LATAM

This residency is not designed for beginners or engineers without prior production exposure.

Program Logistics

• Start Date: Ongoing for the first quarter of 2026
• Program Type: Full-time, paid residency
• Focus: Execution-first, production-grade AI systems
• Duration: 12 to 16 weeks
  
  

Factored does not charge upfront fees to participate. Any contractual terms related to participation and long-term engagement are communicated transparently during the process.

Admission Process

The admission process is designed to evaluate real-world readiness and system-level thinking.

• Online Assessment: Evaluation of core technical foundations and problem-solving ability
• Talent Interview: Assessment of communication, clarity of thought, and professional alignment
• Technical Interview: Deep dive into prior production experience and decision-making
• System Design Interview: Evaluation of architecture thinking, trade-offs, and cultural fit
  
  

Admission is selective by design.

We are seeking a Machine Learning Engineer who is passionate about building state-of-the-art recommender systems and leveraging Generative AI. You'll work with large-scale data using tools like Databricks and Spark, contributing to innovative AI solutions that enhance personalized experiences while being part of a supportive, dynamic, and collaborative team.  In return, you will be rewarded with an amazing team that supports you, a rich culture, shared success, and the flexibility to work– from the comfort of your home.

Functional Responsibilities:

• Design and implement recommender systems to improve product discovery and enhance customer engagement across digital and physical platforms.
• Build and manage scalable machine learning pipelines for data processing, feature engineering, model training, and deployment using tools like Databricks and Spark.
• Apply and optimize advanced machine learning models for recommendation systems, including Wide & Deep models, Two-Tower architectures, Transformer-based models (e.g., NRMS), embeddings-based approaches, neural networks, autoencoder-based models (e.g., AutoRec), and deep sequential models like GRU4Rec.
• Collaborate closely with software engineers, data scientists, and business stakeholders to integrate models into production systems and solve real-world business challenges.
• Monitor, maintain, and continuously enhance deployed models to ensure reliability, accuracy, and alignment with evolving business needs.
• Stay informed on the latest advancements in machine learning, recommender systems, deep learning, and Generative AI to drive innovation and improvement.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• 5+ years of proven experience as a Machine Learning Engineer, demonstrating successful development and deployment of Machine Learning models. 
• Minimum 1 year of hands-on experience designing, building, and deploying recommender systems. This is a must-have requirement.
• Strong programming skills in languages such as Python along with experience with machine learning libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.S
• olid understanding and application of machine learning techniques relevant to recommendation systems, including but not limited to Wide & Deep models, Two-Tower models, Transformers, embeddings, neural networks, autoencoders (AutoRec), and deep sequential models (GRU4Rec)
• Extensive experience handling large-scale data processing and analysis using Spark/PySpark within Databricks, including its native platform services.
• Solid understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
• Strong knowledge of experimental design, A/B testing, and performance evaluation metrics for machine learning solutions.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (Docker) is a plus.
• Excellent verbal and written communication skills in English.

We are seeking a skilled Machine Learning Engineer to join our team, with a specialized focus on Retrieval-Augmented Generation (RAG) models. The ideal candidate will have experience in designing, developing, and deploying advanced machine learning models that integrate retrieval and generation capabilities to create powerful AI-driven applications.

Functional Responsibilities:

• Design, develop, and optimize Retrieval-Augmented Generation (RAG) models that integrate retrieval-based and generation-based approaches to solve complex, real-world problems for our high-profile clients.
• Improve the performance of RAG models through cutting-edge algorithms, innovative techniques, and model fine-tuning.
• Collaborate with client Data and Engineering teams to establish and build robust machine learning infrastructure to meet project goals.
• Work closely with leadership teams from our clients to identify and leverage AI/ML opportunities that can provide transformative solutions.
• Fine-tune and adapt large language models (LLMs) for specific tasks and domains within the RAG framework.
• Partner with cross-functional client teams to deploy RAG models into production environments, ensuring seamless integration and long-term success.
• Apply advanced machine learning techniques, including LLMs, to develop effective AI solutions tailored to client needs.
• Write clean, maintainable, and scalable code, ensuring all development is well-documented and testable.
• Prioritize user experience and customer needs in all product development efforts.
• Design and develop frameworks for GenAI products, such as search interfaces, chatbots, and summarization tools.
• Build and implement machine learning models and algorithms that directly contribute to client growth and success through innovative, AI-driven solutions.
• Provide technical leadership in identifying and evaluating AI/ML opportunities that empower clients to deliver exceptional solutions.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 5+ years of hands-on experience developing and deploying machine learning models in production environments.
• 4+ years of experience with production NLP and deep learning models using frameworks like PyTorch and TensorFlow.
• At least 1+ year of experience with Retrieval-Augmented Generation (RAG) and other advanced techniques to optimize model performance.
• Proven experience writing production-level code, with strong proficiency in Python.
• Expertise in working with large language models (LLMs) such as GPT, Gemini, and Claude, along with proficiency in LLM frameworks like LangChain.
• Strong understanding of prompting techniques, and the trade-offs between prompting and fine-tuning.
• Experience with cloud platforms such as AWS or GCP (AWS preferred), or equivalent on-premise platforms.

We are looking for a MLOps Engineer to join our team. You will participate in the development and maintenance of AI products for our clients. At Factored we are building a company that we all hold as our own, every single one of us. We need your skills to help take this rocketship to new heights and help create new opportunities for us.  In return, you will be rewarded with an amazing team that supports you, rich culture, shared success and the flexibility to work– from the comfort of your home.

Functional Responsibilities:

• Deploy machine learning models to production environments using industry best practices.
• Design and implement automated workflows for model training, testing, and deployment.
• Manage and optimize the infrastructure supporting machine learning workloads.
• Establish monitoring systems for deployed models to ensure performance and reliability.
• Work closely with data scientists, software engineers, and cross-functional teams to understand model requirements and business objectives.
• Ensure compliance with industry regulations and data protection standards.
• Stay informed about the latest advancements in MLOps and integrate relevant technologies and methodologies.
• Continuously optimize processes to enhance efficiency and reduce deployment times.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• Proven experience as MLOps Engineer, demonstrating successful development and maintenance of machine learning models.
• Strong programming skills in languages such as Python, R, or Java, along with experience with machine learning libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
• Experience with cloud platforms (e.g., AWS, Azure, GCP), Kubernetes, and Docker.
• Excellent verbal and written communication skills in English.