Own end-to-end ML systems from data and training to evaluation, inference, and deployment.
Build and evolve training, fine-tuning, and inference infrastructure for large models.
Partner with research and application engineering to turn model capabilities into product improvements.
Next Step Systems is an IT recruiting and staffing agency that connects companies with machine learning and technology talent. They specialize in placing candidates in remote and on-site roles across the United States and emphasize a fast-paced, results-oriented culture.
Design and build systems that improve the efficiency of ML training and inference workloads.
Develop tooling and benchmarking frameworks to help ML engineers debug, profile, optimize, and monitor model performance.
Partner with ML teams to optimize distributed training, reduce costs, and drive technical strategy for scalability and reliability.
Reddit is a community of communities where users share interests and authentic conversations. With 100,000+ communities and ~130 million daily visitors, it has a flexible-first culture supporting remote work.
Design high-performance training platform components including orchestration, observability, and performance tuning.
Deliver end-to-end ML pipelines from dataset curation to training, validation, and deployment.
Drive technical direction across ML Platform, Infrastructure, Autonomy, and Safety teams.
Stack is developing revolutionary AI and advanced autonomous systems for safer, more reliable, and efficient operations, focusing on autonomous trucking. The Stack team brings decades of experience in real-world systems and is committed to a culture of inclusion, entrepreneurship, and innovation.
Lead and mentor a team of software engineers building scalable machine learning infrastructure.
Define and own the multi-quarter technical strategy for data generation, orchestration, and distributed training.
Represent the Scalable ML team in org-level planning and architecture decisions, resolving cross-team tradeoffs on shared infrastructure.
Latitude AI is building the future of Ford's autonomy roadmap, developing a fully in-house hands-free ADAS platform. As a Ford Motor Company subsidiary, it operates independently like a technology startup with engineering centers in Pittsburgh, Dearborn, and Palo Alto.
Build core machine learning systems that power a proactive, long-horizon AI product.
Own work end-to-end, from data preparation through training, evaluation, inference, and iteration.
Debug model failures using production signals and iterate quickly to ship reliable systems.
Gina's Tech Jobs is an IT recruiting and staffing agency that connects companies with technology professionals. They help organizations hire for roles such as machine learning engineers and other IT positions across the United States.
Lead the effort to make Runpod the fastest and most cost-efficient place for LLM inference, owning performance end to end.
Profile and diagnose performance bottlenecks across the serving stack, from scheduling to kernels, and implement fixes.
Work closely with product and infrastructure teams to shape how inference is offered, turning improvements into production-ready defaults.
Runpod is the AI Developer Cloud, providing a platform for over one million developers to experiment, train, fine-tune, deploy, and scale AI. We're a small, remote-first team that takes ownership seriously, moves fast, and has processed more than 20 billion inference requests.
Lead the end-to-end machine learning lifecycle, from data preparation and model training to evaluation, inference, and deployment.
Build and optimize scalable training, fine-tuning, and inference pipelines for large-scale models on GPU infrastructure.
Establish production best practices for model quality, reliability, safety, and measurable performance improvements.
This company builds production-grade machine learning systems and large-scale GPU-powered inference pipelines. It is a fully remote team that values technical rigor, collaboration, and hands-on engineering.
Owns the architectural direction for Code Metal's AI platform and the team building it.
Builds production-grade inference serving, model gateways, agent harnesses, and context-engineering services.
Instruments the stack end to end and drives experimentation and observability for reliable AI systems.
Code Metal provides automated software engineering using AI and formal verification to produce provably correct code. Founded in 2023 and backed by Accel, Salesforce Ventures, and others, it serves defense and enterprise customers from offices in Boston and San Francisco.
Develop advanced ML models and agentic workflows to accelerate model development.
Use AI-assisted tools like Claude and Cursor to investigate model behavior and automate analysis.
Set technical direction, mentor engineers, and raise the bar for modeling rigor.
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. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.
Build and maintain core infrastructure for Quora's ML platform, ensuring high availability, scalability, and performance.
Build and improve distributed systems serving ML models in production, from Large Recommendation Models to Large Language Models.
Work on GPU model serving, optimizing latency, throughput, and cost to support larger and more capable models.
Quora's mission is to grow the world's collective intelligence through two platforms: Quora for global knowledge sharing and Poe for AI agent collaboration. We are a remote-first company with passionate, collaborative, and high-performing global teams, rooted in transparency and experimentation.
Architect, design, build, deploy, and maintain Model Serving infrastructure for a world-class Detection Engine.
Own projects that scale model serving and data processing to handle 10x traffic, including real-time streaming pipelines and online feature serving.
Collaborate with MLE and Data Science teams to build the ML Training platform, improving MLE velocity and model precision and recall.
Abnormal protects the humans behind the world's most critical organizations from AI-powered cybercrime. 4,500+ enterprises trust our behavioral AI platform, and we foster a culture of innovation and impact.
Refactor and upgrade existing ML models into standardized, production-ready modular contracts.
Engineer resilient ML workflows with DAG-based orchestration tools like Argo Workflows and Airflow.
Build infrastructure for shadow testing, A/B testing, and structured feedback pipelines for safe deployment.
Smart Working connects skilled professionals with outstanding global teams and products for full-time, long-term roles. It is a remote-first company known as one of the highest-rated workplaces on Glassdoor, fostering a genuine community that values growth and well-being.
Build new functionality within a fast-moving product, taking ambiguous ideas from design to rigorous experimentation within weeks.
Conduct deep-dive research into state-of-the-art LLM orchestration, retrieval strategies, and data-driven personalization.
Build production-grade ML systems at Twilio scale, leveraging the latest AI development stacks to focus on core differentiation.
Twilio is a cloud communications platform that enables businesses to create personalized customer experiences through APIs and AI-driven solutions. The company is remote-first, serving hundreds of thousands of businesses, and fosters a culture of connection, inclusion, and innovation.
Own and improve the platform that processes customer jobs, focusing on speed, reliability, and resource efficiency.
Partner with machine learning teams to serve models effectively and integrate them into production workflows.
Build and maintain infrastructure for databases, task orchestration, workload scheduling, and cloud deployments.
They build an AI document processing product and operate its platform infrastructure. Company size and culture are not specified, but engineers work closely with ML teams and own platform reliability.
Design and implement cutting-edge machine learning algorithms and model architectures.
Collaborate with product, engineering, and analytics teams to integrate AI features across Atlassian products.
Conduct rigorous experimentation, model evaluation, and mentor emerging ML engineers.
Atlassian creates team collaboration software like Jira, Confluence, and Trello, aimed at unleashing the potential of every team. They are a distributed-first company with thousands of employees, emphasizing diversity, inclusion, and flexible work arrangements.
Lead the development and optimization of machine learning models to detect and prevent AI security risks like prompt injection and jailbreaks.
Build reproducible training and evaluation pipelines on Reddit's ML platform, partnering with platform engineers to improve performance and reliability.
Set the technical vision and multi-quarter modeling roadmap, mentoring engineers and establishing best practices for responsible ML development.
Reddit is a community of communities, built on shared interests and authentic conversations, with 100,000+ active communities and 130 million daily active visitors. It is one of the internet's largest sources of information, fostering a culture of openness and trust.
Build and maintain production-grade machine learning infrastructure for AI governance and trust, including model serving, evaluation, and monitoring.
Develop low-latency inference systems, model registries, and evaluation pipelines for reliable model lifecycle management.
Design and implement red-team testing, drift monitoring, and continuous retraining loops using telemetry and audit data.
The company builds AI governance and trust infrastructure, including production ML systems for AI agents. It offers a remote-first, international engineering organization with a focus on security and reliability.
Design, build, and deploy production ML and LLM-based systems for enterprise clients.
Own technical delivery end-to-end from architecture to deployment and iteration.
Mentor and support other ML engineers through code reviews and technical guidance.
TensorOps is a boutique AI consultancy that designs and ships production-grade AI systems for enterprise clients. The company has shipped AI systems impacting 200M+ end users daily, partnered with 11 unicorns, and operates fully remotely with a supportive, fast-growing culture.
Design and build autonomous AI agents using modern agentic frameworks to analyze infrastructure and make intelligent decisions.
Develop and deploy ML models and MLOps pipelines that learn from infrastructure patterns to optimize resource policies and scaling.
Own AI systems end-to-end, from architecture to production, ensuring performance, reliability, and cost-effectiveness.
ScaleOps is redefining autonomous cloud and AI infrastructure by freeing DevOps teams from manual resource management, reducing cloud costs by up to 80%. Backed by over $210M from leading VCs, the company is trusted by Fortune 100 companies and fosters an innovative, mission-driven culture.
Design and develop large-scale platforms for LLM training and AI workloads.
Tackle distributed-systems challenges including intelligent job scheduling and resource optimization.
Collaborate with international teams to build production-ready AI infrastructure.
This role is with a partner company, an AI-focused R&D team building infrastructure for large language models. They are a fast-moving, highly technical team with a collaborative and innovative culture.