Lead and evolve the Software Engineering Center of Excellence by designing scalable operational frameworks and managing complex software projects. Coach engineering leads and engineers while ensuring high-quality delivery and alignment with client expectations.
Factored
17 Remote Job Openings at Factored
Design and implement production-grade backend services in Go that integrate with LLMs and event-driven architectures using Kafka. Build AI-augmented CI/CD pipelines and agentic workflows to power enterprise-scale AI-native platform workflows.
Design and manage AWS cloud infrastructure optimized for AI/ML workloads and build CI/CD pipelines for GenAI applications. Implement monitoring, observability, and SRE principles to ensure the scalability and reliability of production AI services.
Design and implement knowledge graph architectures using Neo4j or RDF models to drive AI product development. Integrate these graphs with LLMs using RAG architectures and build robust APIs for network traversal and relationship analysis.
Manage a portfolio of strategic enterprise accounts to drive customer satisfaction, retention, and revenue expansion. Act as a trusted advisor to clients while collaborating with engineering leads to mitigate delivery risks and align objectives.
Drive the technical evolution of the Data Engineering practice by bridging business development with technical execution. Manage engineering talent through mentorship, performance reviews, and the creation of upskilling programs.
Design and deliver advanced computer vision systems for mission-critical applications, owning models from experimentation to production. Responsibilities include developing models for classification and detection, optimizing performance for cloud and edge devices, and managing scalable deployment pipelines.
The Enterprise Key Account Director will manage and grow strategic client relationships by understanding business needs and driving account expansion. They will lead cross-functional teams to ensure high-quality service delivery, monitor client satisfaction, and align internal resources with client strategic roadmaps.
Design and implement machine learning and statistical models for anomaly detection on large-scale manufacturing test logs. Build and deploy scalable, low-latency production pipelines for real-time detection and post-hoc root cause analysis.
The role involves creating and rewriting professional resumes for technical talent while providing one-on-one coaching for interviewing skills. You will manage multiple resume projects simultaneously and collaborate with recruiters and engineers to ensure high-quality, keyword-optimized content.
The Analytics Manager will own and evolve the Center of Excellence by designing scalable operational frameworks and managing complex data projects. They will also mentor engineering leads, drive process improvements, and ensure high-quality technical delivery across the organization.
Manage and optimize the end-to-end engineering onboarding pipeline while serving as the operational lead for talent enablement programs. Collaborate cross-functionally to ensure seamless candidate placement and deliver data-driven insights to leadership regarding pipeline health.
The Enterprise Development Partner will develop and execute strategic outbound plans targeting enterprise accounts and engage senior executives through personalized outreach strategies. This role involves generating qualified opportunities and partnering closely with Sales and Marketing to refine messaging and campaigns.
The role involves designing and implementing state-of-the-art recommender systems to boost product discovery and customer engagement across platforms. Responsibilities include building scalable machine learning pipelines using tools like Databricks and Spark, and applying advanced models such as Wide & Deep and Transformer-based architectures.
The Machine Learning Engineer will design, develop, and optimize Retrieval-Augmented Generation (RAG) models to solve complex problems for clients. They will collaborate with client teams to deploy these models into production environments and ensure their long-term success.
Participants will engage in a rigorous, full-time training program that includes working on capstone projects based on real-world problems. They will receive feedback and mentorship to prepare for high-impact AI projects with top-tier clients.
The MLOps Engineer will deploy machine learning models to production environments and design automated workflows for model training and deployment. They will also manage infrastructure and establish monitoring systems for model performance.