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.
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.
Prototype new training frameworks and production-ize solutions at scale.
Design, optimize and test model integration infrastructure.
Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision, natural language processing, LLM's and audio recognition. Clarifai was founded in 2013 and has employees remotely based throughout the United States, Canada, Argentina, India and Estonia.
Work with research teams to design and build our training infrastructure
Prototype new training frameworks and production-ize solutions at scale
Design, optimize and test model integration infrastructure
Clarifai is a leading AI platform specializing in computer vision, NLP, LLMs, and audio recognition, helping organizations transform unstructured data into structured data. Founded in 2013, they remotely operate across multiple countries with backing from industry leaders, fostering a diverse and equal opportunity workplace.
Lead and grow a high-performing team of backend and machine learning engineers. Evolve the search architecture that connects users with the perfect template. Deliver measurable improvements to search quality across surfaces like homepage and query-based discovery.
Combine Software Engineering and Data Science disciplines to create production-ready Machine Learning models. Develop frameworks and platform to build, deploy, serve and monitor ML-based services. Contribute to vision and architecture to scale ML solutions at QuintoAndar's business.
We are Grupo QuintoAndar, the largest real estate ecosystem in Latin America, guided by a shared purpose of helping people love the place they live.
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.
Focus on data ops, ML development pipeline, logging & aggregation.
Torc has been a leader in autonomous driving since 2007. Now a part of the Daimler family, they are focused solely on developing software for automated trucks to transform how the world moves freight. Their culture is collaborative, energetic, and team focused.
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.
Develop scalable MLOps pipelines for model training, validation, deployment, and monitoring using AWS services
Implement infrastructure as code and CI/CD workflows to support rapid experimentation and reliable production releases
Collaborate with data scientists to productionize ML models and ensure reproducibility, versioning, and traceability
Experian is a global data and technology company, powering opportunities for people and businesses around the world. A FTSE 100 Index company listed on the London Stock Exchange (EXPN), they have a team of 23,300 people across 32 countries and corporate headquarters are in Dublin, Ireland.
Continuously improve the performance and scalability of ML models. Build and deploy models from inception to live in production pipelines. Advocate for code and process improvements across your team, and help to define best practices based on personal industry experience and research.
BenchSci's mission is to exponentially increase the speed and quality of life-saving research and development.
Implement production AI / ML workloads using Ray and Anyscale.
Advise customers on ML system architecture.
Partner with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows.
Anyscale is on a mission to democratize distributed computing and make it accessible to software developers. They are commercializing Ray, an open-source project creating an ecosystem of libraries for scalable machine learning and are backed by Andreessen Horowitz, NEA, and Addition.
Act as a trusted advisor to customers, building relationships with technical and business stakeholders.
Advise on GenAI and ML best practices and give product demos to stakeholders.
Partner with product and engineering teams to drive the product roadmap and spearhead new opportunities within existing accounts.
Arize AI is the leading AI & Agent Engineering observability and evaluation platform, empowering AI engineers to ship high-performing, reliable agents and applications.
Design, build, and scale enterprise-grade AI/ML systems that power internal workflows and external-facing AI/ML platforms.
Develop a production-ready Generative AI and MLOps platform with reusable components used to deploy multiple AI solutions across Natera’s business units.
Implement cloud-native infrastructure for large-scale model training and serving using Kubernetes, MLflow, Terraform, and AWS-native services
Natera is a global leader in cell-free DNA (cfDNA) testing. They are dedicated to oncology, women’s health, and organ health, aiming to make personalized genetic testing and diagnostics part of the standard of care. The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions.
Train, evaluate, and optimize machine learning models for high performance.
Contribute to R&D in object detection and multi-object tracking for remote sensing.
Design and deliver production-grade, maintainable code while managing multi-phase development.
Clarifai is a leading AI platform specializing in computer vision and generative AI. They empower organizations to transform unstructured data into actionable insights. Their globally distributed team operates across the United States, Canada, Estonia, Argentina, and India and is committed to building a diverse and inclusive team.
Establish the technical vision for personalization.
Solve the company’s most complex ML challenges and influencing strategy, architecture, and innovation across teams.
Design and implement advanced ML solutions using cutting-edge techniques .
Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState.
Lead development stages for AI/ML projects from exploration to maintenance.
Design and implement scalable ML pipelines for large datasets with data scientists and network security experts.
Conduct experiments and analyze results using metrics and visualization techniques.
Corelight is a cybersecurity company that transforms network and cloud activity into evidence for elite defenders. Fueled by accelerating revenue and investments from top-tier venture capital organizations, they are rapidly expanding their team with a geographically dispersed yet connected employee base.
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.