Design, build, and operate reliable, scalable backend services and data pipelines.
Work across our cloud infrastructure (AWS) to ship and run production systems.
Improve the performance, reliability, and observability of the systems you own.
Allocate is transforming private market investing by enabling RIAs and family offices to seamlessly discover, model, and manage their private market exposure. They are a company that values world-class client experience, challenges convention, and embraces continuous improvement and meritocracy.
Design, develop, and maintain scalable backend services, APIs, and cloud-native applications using Python.
Build and optimize distributed systems, payment processing workflows, and backend infrastructure.
Mentor engineers through code reviews, technical guidance, and knowledge sharing.
The company develops AI-driven financial technology solutions for businesses globally. It offers a fully remote work environment with a collaborative and innovative engineering culture.
Design and build scalable backend services using Python, AWS, and Kubernetes to improve developer productivity.
Lead technical initiatives across the engineering organization with 7–10 years of experience.
Work on production AI/LLM applications and microservices architecture in a fully remote European team.
Codeminders develops cutting-edge software solutions for high-tech companies, focusing on AI, mobile apps, video conferencing, and cloud computing. They collaborate with world-class engineers from the US and Ukraine and uphold strong ethical standards with no business ties to Russia or Belarus.
Design and build scalable backend services for ML feature management, storage, and serving.
Own technical initiatives from design through implementation, balancing trade-offs and navigating ambiguity.
Collaborate with cross-functional teams to define solutions that align with business objectives and improve platform reliability.
The company is a technology firm focused on building scalable machine learning platforms and intelligent decision-making systems. It operates in a remote-first environment with a collaborative culture and a commitment to engineering excellence.
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.
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.
Build and scale massive distributed compute and storage systems for AI training.
Architect multi-cluster orchestration and optimize workload placement across global regions.
Design future-proof storage formats and implement metadata systems for exabyte-scale growth.
Mistral provides full-stack AI solutions, from frontier models to developer tools. They are a dynamic, collaborative team with a diverse workforce, passionate about innovation and low-ego teamwork.
Design, build, and enhance the infrastructure powering a large-scale real-time data ingestion platform.
Scale backend services to support increasing data volumes while maintaining high availability and performance.
Automate infrastructure provisioning and deployment pipelines using Infrastructure as Code.
Our partner builds a high-performance real-time data platform operating at petabyte scale. They are a remote-first organization with a collaborative engineering culture.
Design and build backend services for AI-powered product features, including inference pipelines and orchestration layers around LLMs.
Develop high-throughput, low-latency distributed systems with monitoring, logging, and alerting across production services.
Collaborate with product, infrastructure, and AI engineers to optimize performance, caching, batching, and streaming.
This team is building an AI-native productivity platform that replaces repetitive digital work with reliable AI workflows. They are a small, focused product team working on cutting-edge AI infrastructure.
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 and launch backend systems at scale using Python or Kotlin.
Collaborate with product management, design, and analytics to define technical plans and articulate constraints.
Foster a culture of quality and ownership through code reviews, design standards, and tech talks.
Affirm is reinventing credit to make it more honest and friendly, offering consumers the ability to buy now and pay later without hidden fees. The company is a global fintech with a remote-first culture, known for its agile and fast-paced environment, serving over 20 million users.
Define technical strategy for your team on a year-long scale, tying it to critical business projects.
Collaborate across teams in product development, ensuring technical sustainability and managing trade-offs.
Foster a culture of quality and ownership through code review standards, design advocacy, and mentorship.
Affirm is a financial technology company that offers buy now, pay later services without hidden fees. They are a remote-first company with a global engineering team, focused on honest and friendly credit.
Design, develop, and deploy production-grade AI-powered backend systems.
Integrate large language models and machine learning models into scalable architectures.
Optimize system performance and implement strong testing practices.
Our partner company is building advanced AI-powered systems to create meaningful customer value. The team operates in a high-autonomy, fast-moving environment focused on production-ready AI solutions.
Build AI platform and production systems supporting computer vision, perception, simulation, and mapping products.
Own critical parts of the AI platform, including orchestration, APIs, and deployment patterns.
Partner with data scientists and simulation engineers to reduce iteration friction and design production architecture.
HERE Technologies is a location data and technology platform company that empowers customers to achieve better outcomes. The company is an equal opportunity employer with a focus on innovation and inclusion.
Own and deliver quarterly goals for your team, leading engineers through ambiguity to solve open-ended problems.
Support peers and stakeholders in product development by collaborating with product management, design, and analytics on technical constraints and trade-offs.
Foster a culture of quality and ownership by setting code review and design standards, and help develop talent through feedback and guidance.
Affirm is a fintech company reinventing credit to be more honest and friendly, offering consumers the flexibility to buy now and pay later without hidden fees or compounding interest. The company is remote-first and values its people, providing competitive benefits and a culture of ownership and quality.
Design, develop, test, monitor and maintain backend services for our systems.
Build clean, efficient, and well-documented APIs for web and mobile applications.
Write high-quality, maintainable code with a focus on reliability, scalability, and performance.
Midnite is a next-generation sports betting and gaming platform built for a new wave of players. With over 400,000 players and a high-performance team, we operate at pace with high ownership and constant iteration.
Own and scale distributed systems for orchestration, APIs, and data paths.
Apply query-engine expertise to optimize work execution and data movement.
Exercise strong technical judgment on ambiguous problems.
Prefect builds and operates resilient, Pythonic orchestration and MCP platforms used for mission-critical workloads. This remote-first company fosters a supportive, high-performance culture that empowers team members to do their best work.
Design and build scalable backend systems, APIs, and data pipelines
Own services end-to-end from architecture, development, deployment, to operation
Mentor junior engineers and contribute to a strong engineering culture
We transform vulnerability intelligence by helping security teams act faster with more confidence. We have a transparent, collaborative culture with a small, smart, and supportive team built on over two decades of cybersecurity experience.
Own end-to-end technical execution for strategic customer and partner engagements, including discovery, infrastructure design, implementation, and production deployment.
Design and build cloud infrastructure supporting advanced AI workloads, including simulation, training, evaluation, inference, and large-scale batch processing.
Improve platform reliability, security, performance, and cost efficiency by debugging issues across application, network, storage, compute, and orchestration layers.
The partner company is building the infrastructure foundation for next-generation AI applications and physical AI workloads. The engineering team is pioneering and values ownership, technical excellence, and solving challenging engineering problems at scale.