Design, implement, and maintain ML-driven services and data workflows in Python.
Apply software engineering best practices, including clean code, testing, CI/CD, and documentation.
Deploy and operate ML services on GCP, using tools like Cloud Run, GKE, and BigQuery.
CertifyOS builds the data infrastructure that powers modern healthcare, automating provider licensing, enrollment, and credentialing via an API-first platform. They are backed by leading investors and built by a team with deep experience in provider data systems, emphasizing authenticity, accountability, and collaboration.
Design, develop, and deploy AI agents and automation workflows powered by LLMs.
Integrate AI capabilities with enterprise platforms like CRM, ERP, and databases on Google Cloud Platform.
Build CI/CD pipelines, monitoring, and observability to ensure production scalability and reliability.
Jobgether is an AI-powered job matching platform that connects candidates with hiring companies using automated evaluation processes. They foster an inclusive culture and support professional growth in a collaborative environment.
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.
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.
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.
Design, develop, and deploy production-ready generative AI and machine learning applications.
Collaborate with cross-functional teams to integrate AI capabilities into business systems.
Apply prompt engineering, retrieval-augmented generation, and evaluation strategies to optimize AI performance.
The company is a technology firm specializing in AI transformation initiatives. It fosters a collaborative culture centered on learning, innovation, and professional development.
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.
Develop, train, and evaluate machine learning models to support product objectives.
Prepare, clean, and analyze datasets for model training and validation.
Deploy, monitor, and improve machine learning models in production environments.
The partner company develops AI-powered products to solve real-world challenges. They offer a fully remote, flexible work environment with a focus on innovation and collaboration.
Design, build, and operate scalable infrastructure platforms for large-scale AI model training and inference.
Manage and optimize GPU clusters, distributed training environments, and scheduling systems for machine learning applications.
Develop software solutions and automation tools using Python and systems programming languages like Go or C++.
Our partner builds and operates foundational technology powering advanced AI training and inference workloads at scale. They offer a collaborative culture focused on innovation, engineering excellence, and continuous learning.
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, build, and maintain scalable data pipelines for ingestion and transformation.
Work with Python, SQL, Apache Airflow, and Google Cloud Platform.
Collaborate with cross-functional teams to deliver reliable and high-quality data solutions.
Our partner is a technology company focused on AI and data solutions. The team values collaboration, innovation, and continuous learning, and offers a remote work environment.
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.
Design and build the AI execution platform with event-triggered workflows and model-agnostic runtimes.
Develop evaluation layers with golden test suites and safety checks to ensure AI reliability.
Implement governance mechanisms and build AI agents supporting business workflows.
Jobgether is a platform that connects talent with opportunities using AI-powered matching. The company has a globally distributed team and a remote-first culture.
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, 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.
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.
Design, train, and ship ML systems for governance and security like anomaly detection and trust scoring.
Build data pipelines, model serving, evaluation frameworks, and feedback loops.
Set technical direction, own architecture, and help recruit and mentor as the team grows.
Docker provides developer tooling trusted by over 20 million monthly users and billions of container pulls. They are a globally distributed, remote-first team building tools for software delivery.
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.
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.