Design, deploy, and maintain scalable ML infrastructure for model training, batch processing, and real-time inference.
Build and manage cloud-based infrastructure with AWS and Snowflake using Infrastructure-as-Code practices.
Develop CI/CD pipelines, automation frameworks, and monitoring for ML systems to improve reliability and governance.
Jobgether is an AI-powered job matching platform that connects candidates with hiring companies. They operate with a team-oriented culture and offer remote work flexibility, focusing on efficient, unbiased recruitment.
Lead the development and improvement of MLOps platform capabilities supporting machine learning workflows across Oura.
Drive design and unification of workflows and tooling for reliable training, orchestration, and deployment of ML systems.
Partner with data scientists and engineers to improve the end-to-end ML lifecycle, including governance and maintainability.
Oura empowers people to own their inner potential through its award-winning Oura Ring and app, providing daily health insights. As a quickly growing company, we focus on team well-being and helping people live healthier, happier lives.
Design, build, and optimize data workflows for Machine Learning and GenAI solutions in cloud environments.
Develop and deploy Machine Learning and Generative AI models using AWS SageMaker.
Create and manage data and model pipelines to improve the efficiency of AI and machine learning systems.
Netrix Global provides the people, processes, and technology to run and scale modern data-driven businesses. It is a top system integrator with a culture focused on ownership, teamwork, and respect.
Design and maintain ML model productionization infrastructure for high-visibility product features.
Collaborate with data science to streamline model training, validation, and deployment.
Implement robust monitoring and alerting for model performance, drift, and data quality.
The Athletic is a sports media company powered by one of the largest global newsrooms in sports, delivering in-depth coverage of professional and college teams across North America and Europe. With over 500 full-time staff, they foster a collaborative culture focused on high-quality journalism and data-driven innovation.
Design, build, deploy, and optimize machine learning models that process large volumes of complex, unstructured data.
Develop and maintain scalable ML pipelines capable of supporting millions of documents and diverse customer requirements.
Lead technical initiatives from early experimentation through production implementation and ongoing improvement.
The company develops intelligent automation solutions using AI and machine learning for large-scale document processing. It offers a highly autonomous engineering environment with a focus on continuous improvement and innovation.
Define the technical vision and strategy for the machine learning platform supporting ML and generative AI development.
Set reference architectures and standards for scalable data and ML pipelines, MLOps practices, and production reliability.
Provide technical leadership and mentorship across engineering teams to raise the bar for ML systems.
PointClickCare provides cloud-based healthcare software solutions. With a large engineering team that values collaboration and innovation, they foster a culture of technical excellence and mentorship.
Lead the technical vision and roadmap for the ML platform.
Partner with Product and Engineering leadership to align investments.
Establish MLOps practices and optimize large-scale model training and serving.
The hiring company builds machine learning platforms and MLOps solutions for large-scale AI development. It offers a collaborative and specialized environment with a focus on technical excellence and innovation.
Develop, deploy, and maintain production-grade Machine Learning models in cloud environments.
Build, maintain, and optimize data pipelines and monitor model performance post-deployment.
Partner with engineering, data, and business stakeholders to translate goals into scalable ML solutions.
In All Media is a global technology and design firm building impactful digital solutions through remote, distributed teams across LATAM. They partner with international clients across industries, providing long-term technical expertise and team augmentation.
Design, build, and operate scalable backend services, APIs, and data pipelines for ML-driven personalization.
Improve reliability, performance, and observability of production ML systems, including model versioning and safe rollout.
Collaborate with data scientists and product engineers to translate business needs into robust technical solutions.
Hungryroot uses AI to build a consumer-centric food and wellness company, acting as a personal assistant for healthy living by recommending and delivering healthy groceries, recipes, and supplements. They are a distributed team across 28+ US states with a remote-first culture emphasizing collaboration, flexibility, and an annual company retreat.
Design, deploy, and optimize production-grade machine learning systems for the full ML lifecycle.
Build scalable MLOps platforms, CI/CD workflows, and model serving infrastructure.
Collaborate with engineering teams to improve platform scalability, security, and operational best practices.
They build scalable MLOps infrastructure for enterprise AI solutions. They foster a collaborative, remote-first culture focused on innovation and professional growth.
Build, train, and deploy machine learning models and data pipelines using Python, pandas, NumPy, and scikit-learn, ensuring scalability and reliability in production.
Design, implement, and continuously improve LLM-powered applications and Retrieval-Augmented Generation (RAG) pipelines, including prompt engineering, embedding strategies, and vector store integration.
Conduct exploratory data analysis and design data visualizations to uncover trends and communicate key metrics to both technical and non-technical stakeholders.
Great Gray Group is a leading independent provider of trustee and administrative services for Collective Investment Trusts, with over $370 billion in assets under management. They are a high-growth organization backed by Madison Dearborn Partners, fostering a culture of collaboration, innovation, and continuous improvement.
Lead a team building data platforms and ML operations infrastructure.
Own reliability, scalability, and continuous improvement of analytics systems.
Collaborate with data scientists and leaders to deliver impactful ML solutions.
The company builds advanced data and machine learning platforms that power analytics and intelligent products. Its culture emphasizes innovation, reliability, and inclusivity, supporting professional growth and technical excellence.
Develop highly scalable and reliable data systems on AWS cloud platform for data-centric products.
Collaborate with Data Science teams to incorporate Machine Learning algorithms using Python and data engineering tools.
Lead and mentor team members, contribute to Agile cycles, and ensure timely delivery of documented software.
Experian is a global data and technology company that powers opportunities for people and businesses worldwide. With 22,500 employees across 32 countries, they foster a culture of innovation and data-driven solutions.
Design, build, and operate large-scale ML solutions for ad delivery performance.
Collaborate with cross-functional teams to deploy models into production.
Mentor peers and contribute to ML best practices and AI-native development.
The company is a technology firm focused on AI-driven advertising solutions. It operates in a remote-first environment with a collaborative and inclusive culture.
Develop, deploy, and maintain machine learning models in production environments using AWS SageMaker or similar platforms.
Perform exploratory data analysis, feature engineering, and build data pipelines to support scalable ML workflows.
Monitor production models, address performance issues, and collaborate with engineering and business stakeholders to deliver data-driven solutions.
Google is a global technology company that develops products and services to organize information and make it universally accessible and useful. The company is a large multinational with a culture focused on innovation, collaboration, and data-driven decision-making.
Build end-to-end Machine Learning pipelines, including data preparation, code optimization, automated model training, and prediction workflows.
Provide technical leadership in MLOps processes, establishing best practices for development, deployment, and maintenance.
Design and implement cloud-based architectures for AI and data solutions using platforms such as GCP, AWS, or Azure.
A technology company specializing in data-driven solutions for consumers, leveraging AI and cloud technologies to transform data into insights. The company fosters a dynamic, innovation-driven culture with a remote-first approach, collaborating across multidisciplinary teams.
Design and develop machine learning infrastructure, tooling, and models to deliver world-class experiences.
Help product teams understand the data lifecycle and experimental nature of machine learning.
Build internal platforms to enable teams to incorporate AI into customer-facing products.
Weave provides AI-powered features and data services, handling data for hundreds of millions of people daily. The company fosters a culture of autonomy, collaboration, and innovation with cross-functional agile teams.
Author and maintain Terraform for Snowflake resources across a multi-account, multi-region footprint.
Contribute to role-based access control, schema migration tooling, and CI/CD pipelines for data warehouse operations.
Build and operate customer-facing financial reporting APIs and PCI-scoped ETL pipelines on AWS and Snowflake.
Marqeta is a modern card issuing platform powering companies like Uber, Square, Instacart, and Klarna. They are a publicly-traded company with a focus on innovation and collaboration, offering flexible work and a range of benefits.
Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients.
Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration.
Mentor and support other ML engineers on the team with code reviews, technical guidance, and knowledge sharing.
TensorOps is a boutique AI consultancy that bridges strategy and execution, designing and shipping production-grade AI systems for enterprise clients. We are a 100% remote team of 11+ people, partnering with unicorns and NASDAQ-listed companies, and have a culture of autonomy, open communication, and continuous learning.
Own end-to-end delivery quality for major engagements, translating ambiguous client needs into practical execution plans.
Lead solution architecture and technical decision-making, making pragmatic tradeoffs between speed, quality, and client value.
Build and ship production AI/ML systems using Python, ML frameworks, and cloud-native infrastructure while mentoring other engineers.
Eliza is a technology services company and Advanced-tier OpenAI partner that helps organizations build and deploy AI solutions, from generative AI to predictive analytics. They are a collaborative, mission-driven team focused on real-world AI impact.