Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
At JetBrains, code is their passion and they strive to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in their IDEs.
Design, build, and optimize high-performance systems in Python supporting AI data pipelines and evaluation workflows
Develop full-stack tooling and backend services for large-scale data annotation , validation, and quality control
Improve reliability, performance, and safety across existing Python codebases
Alignerr connects top technical experts with leading AI labs to build, evaluate, and improve next-generation models. They work on real production systems and high-impact research workflows across data, tooling, and infrastructure.
Design, build, and optimize high-performance systems in Python supporting AI data pipelines and evaluation workflows.
Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control.
Improve reliability, performance, and safety across existing Python codebases.
Alignerr connects top technical experts with leading AI labs to build, evaluate, and improve next-generation models. They work on real production systems and high-impact research workflows across data, tooling, and infrastructure.
Own the end-to-end lifecycle of ML model deployment—from training artifacts to production inference services.
Design, build, and maintain scalable inference pipelines using modern orchestration frameworks (e.g., Kubeflow, Airflow, Ray, MLflow).
Implement and optimize model serving infrastructure for latency, throughput, and cost efficiency across GPU and CPU clusters.
MARA is building a modular platform that unifies IaaS, PaaS, and SaaS which will enable governments, enterprises, and AI innovators to deploy, scale, and govern workloads across data centers, edge environments, and sovereign clouds. They are redefining the future of sovereign, energy-aware AI infrastructure.
Design and maintain infrastructure supporting scalable real time data pipelines to handle huge datasets. Develop and support tooling enabling implementation of custom ML algorithms in a low latency environment. Work on infrastructure for running training, inference, monitoring, and deployment on thousands of ML tasks concurrently.
StackAdapt empowers marketers to reach, engage, and convert audiences with precision with its AI-powered marketing platform.
As a Senior MLE, debug complex AI implementations and optimize inference performance. Work directly with product teams building solutions and develop blueprints for proven patterns. Operate in a high-velocity environment where priorities shift rapidly based on team needs.
Join the team redefining how the world experiences design.
Build backend and pipeline systems that turn models into real search experiences for 110M+ daily users, owning data flows, ranking and retrieval services, and low-latency model-serving APIs. Integrate models into production through robust interfaces and DAGs, enabling fast iteration and powering discovery across the internet’s largest community platform. Ensure pipelines and systems support high scale, low latency, and operational excellence.
Reddit is a community of communities built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet.
Design, implement, and deploy AI-powered features, including model training, fine-tuning, and prompt engineering workflows.
Translate product requirements into robust, production-ready AI solutions, working with Product Managers, Software Engineers, and Data Scientists.
Optimize models and infrastructure for scalability, latency, and cost efficiency, partnering with DevOps and MLOps to ensure reliable and maintainable AI pipelines.
Paper is reimagining how schools support students so that every learner can reach their full potential.
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale.
Design ranking algorithms that balance relevance, diversity, and revenue.
Run statistically rigorous A/B tests to measure true business impact.
Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState.
Optimize ad performance using both mature models and emerging ML technologies.
Build scalable infrastructure to support real-time ad decisioning across millions of requests per day.
Collaborate with global teams across product, data, and engineering to launch high-impact ad features.
Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState.
Enable teams to build features at scale by providing a foundation of reusable software components and infrastructure.
Motive empowers the people who run physical operations with tools to make their work safer, more productive, and more profitable. Motive serves nearly 100,000 customers – from Fortune 500 enterprises to small businesses – across a wide range of industries.
Challenge advanced language models on realistic infrastructure and platform scenarios.
Verify architectural soundness and logical correctness, assess code quality and testing strategies.
Analyze performance bottlenecks and deployment risks, capture reproducible failure cases, and suggest improvements.
The company is hiring for a SWE Infrastructure Specialist. As a contractor, the employee will need to supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply.
Design and implement interfaces across the platform for compute orchestration and RL training.
Translate complex backend systems into intuitive, production-ready product experiences.
Build for technical audiences, including AI and general software engineers.
Prime Intellect makes frontier AI accessible to everyone and enables individuals/organizations to train models using their agentic training infrastructure.
Draft detailed natural-language plans and code implementations for machine learning tasks. Convert novel machine learning problems into agent-executable tasks for reinforcement learning environments. Identify failure modes and apply golden patches to LLM-generated trajectories for machine learning tasks.
Mercor is building the talent engine that helps leading labs and research orgs move AI forward.
Lead AI and ML initiatives to design and implement production-grade machine learning systems and pipelines. Develop scalable infrastructure for model training, evaluation, and deployment, ensuring reliability and observability. Collaborate with cross-functional teams to drive innovation and efficiency.
Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.