Productionize ML models into reliable, scalable systems with CI/CD, data versioning, and model governance.
Implement monitoring for data quality, drift, model performance, and pipeline health with clear alerting.
Refactor research code into reusable components and enforce engineering best practices.
Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years, backed by Spark Capital & Foundry.
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.
Develop tools and automate manual processes to improve operational efficiency and accelerate experimentation.
Build, integrate, and monitor end-to-end lifecycles of large-scale distributed machine learning systems.
Enhance ML pipelines for forecasting platforms with automated retraining and deployment.
Apella applies computer vision and machine learning to improve the standard of care in surgery. They are a remote-first startup focused on building reliable ML solutions for healthcare.
Provide on-call MLOps support for model development teams, triaging and resolving pipeline issues.
Assist in debugging model workflows and identifying root causes across the ML stack.
Collaborate with senior engineers to improve tooling, documentation, and operational processes.
Torc is an autonomous driving leader since 2007 and now part of Daimler, building software for automated trucks. The culture is collaborative, energetic, and team-focused.
Design, develop, and maintain MLOps pipelines on AWS to automate and operationalize machine learning models.
Collaborate with Data Scientists to deploy, monitor, and scale ML solutions in production environments.
Architect cloud-based systems, integrate APIs, and build web applications and LLM-powered agents.
The company is a partner of Jobgether, specializing in cloud and AI solutions. It fosters a collaborative, innovation-driven environment with a focus on professional development and employee well-being.
Design and maintain CI/CD and MLOps pipelines for AI and software applications, ensuring seamless deployment and automation.
Build and scale cloud-native infrastructure using Kubernetes, Docker, and GPU clusters to support high-performance AI workloads.
Champion Infrastructure as Code and observability practices to ensure high availability, security, and compliance across multi-cloud environments.
Bitdeer is a world-leading technology company providing AI and Bitcoin mining infrastructure. Headquartered in Singapore, the company has a global presence with data centers in multiple countries and a culture focused on innovation and reliability.
Design, develop, and evaluate machine learning models using real-world health data for personalized interventions.
Work with engineering teams to integrate models into production digital therapeutic solutions as Software as a Medical Device.
Ensure models meet regulatory, privacy, and explainability requirements while exploring generative AI applications.
They develop AI and machine learning solutions for digital therapies to improve patient outcomes. They are a technology-focused healthcare company with a remote-first culture.
Design and maintain CI/CD and MLOps pipelines for software and machine learning models.
Build and scale cloud-native infrastructure using Kubernetes, Docker, and GPU clusters.
Champion Infrastructure as Code and observability to ensure high availability and governance.
Bitdeer is a world-leading technology company for AI and Bitcoin mining infrastructure, providing comprehensive solutions and cloud capabilities. Headquartered in Singapore, the company has deployed data centers across multiple countries and fosters a culture of innovation.
Build and operate the Kubernetes platform supporting AI test and evaluation frameworks.
Design infrastructure-as-code, GitOps workflows, and automated deployment pipelines.
Own platform reliability, observability, capacity planning, and operational readiness.
OpenTeams helps enterprises and governments build AI they control, govern, and evolve themselves. Founded by the creator of NumPy and SciPy, the company is built by people with deep roots across the open-source ecosystem and maintains a remote-first culture.
Build and operate the real-time inference service that scores models for the risk decision engine, with low latency and high availability.
Own model deployment infrastructure including registry, versioning, CI/CD, and staged rollouts.
Build model observability with availability, latency, error monitoring, and drift detection.
Mercury is a fintech company that builds banking services for startups. They are committed to diversity and inclusion, and are an equal opportunity employer.
Design, build, deploy, and maintain scalable machine learning and AI systems in GCP to solve complex business problems and create new value.
Partner across teams to understand problem definitions, data needs, and solution approaches, and support model deployment with MLOps practices.
Take ownership of production issues, perform root cause analysis, and improve documentation and quality assurance processes for ML systems.
General Mills makes food the world loves, operating across 100+ markets. As a large company, it prioritizes being a force for good and fosters a culture of bold thinking and big hearts, where employees challenge each other and grow together.
Build AI-powered tools and copilots across the SDLC to reduce cognitive load and eliminate manual steps.
Research and deploy GenAI solutions to improve delivery pipelines and system reliability.
Collaborate with Platform, SRE, and DevOps teams to integrate intelligent automation into the core engineering platform.
Coderio designs and delivers scalable digital solutions for global companies. They combine strong technical expertise with a product mindset and value autonomy and clear communication.
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.
Design and maintain reliable, low-latency ML APIs to integrate Safety AI model outputs into cloud applications.
Build scalable data pipelines for continuous model iteration, backtesting, and online evaluation.
Optimize model artifacts for production and monitor rollout health, ensuring predictable failure modes.
Samsara builds a Connected Operations Cloud that helps physical operations use IoT data to improve safety, efficiency, and sustainability. Samsara is a recently public company with an employee-led remote culture and a long-term focus.
Drive and continuously improve MLOps practices across the machine learning environment.
Build, optimize, and maintain CI/CD pipelines using GitLab for reliable ML delivery.
Productionize and maintain machine learning models using AWS, with a focus on SageMaker.
The company operates a globally deployed recommender system and focuses on machine learning infrastructure. The team is collaborative and supportive, with an emphasis on knowledge sharing and professional development.
Own production uptime, latency, provisioning speed, infrastructure cost, and incident response for core platform services.
Build and maintain AWS infrastructure using Terraform, Kubernetes/EKS, Helm, Docker, EC2, CodeBuild, ECR, S3, IAM, networking, and secrets management.
Design and improve backend and platform systems for scale — capacity planning, autoscaling, queueing, backpressure, cleanup jobs, retries, and rollback paths.
A fast-growing AI/ML platform startup building infrastructure for training, evaluating, and aligning AI models within reinforcement learning environments. The engineering team of ~15 includes competitive programming medalists, serial AI startup founders, and researchers published at top venues.
Design and deliver sophisticated ML and data architectures using Linux, Kubernetes, and open-source technologies.
Work closely with sales and technical teams to translate customer requirements into scalable infrastructure solutions.
Influence product direction by sharing customer insights and technical feedback with engineering teams.
The partner company helps organizations adopt modern AI and machine learning technologies across public and private cloud environments. They operate with a global Field Engineering team and a distributed work environment.
Design, develop, and deploy AI/ML and GenAI solutions from concept through production.
Build and optimize models for NLP, forecasting, classification, and anomaly detection.
Collaborate with stakeholders to translate business needs into scalable AI systems.
Our partner company builds and deploys high-impact AI and machine learning solutions across various industries. They offer a remote-first culture and emphasize collaboration and technical excellence.
Automate and build runtime production environments, serving as the link between application development and platform teams.
Validate solutions and implementations to ensure alignment with business requirements and maintain platform integrity.
Independently solve business problems and develop small components to address challenges without explicit architecture diagrams.
Defense Unicorns delivers mission value by streamlining software delivery so our customers can focus on the most important challenges. Our team is composed of innovators, software engineers, and veterans with decades of experience delivering technology programs across the federal market.