Design, implement, and maintain robust, containerized, and reproducible pipelines for model training, evaluation, and deployment—across both batch and real-time settings.
Build and manage ML services, APIs, and model serving infrastructure using tools like MLflow, Amazon SageMaker, and Feature Store.
Set up and maintain monitoring, observability, and alerting systems to ensure high availability and performance (including model/data drift, feature logging, and inference latency).
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.
Take ownership of an ML deployment system spanning multiple production environments and continue to research efficient and effective strategies.
Improve, expand, and streamline our existing deployment pipelines to support faster deployments and automated model retraining.
Collaborate with Data Scientists to understand model requirements and provide guidance to ensure seamless integration with production environments.
Best Egg is a market-leading, tech-enabled financial platform helping people build financial confidence through lending solutions and financial health tools. They foster an inclusive, flexible, and fun workplace with top-tier benefits and growth opportunities.
Own the Pipeline from Cloud to Edge, re-architecting machine learning model deployment to edge devices.
Build Shadow Mode Infrastructure to test candidate models on production devices silently.
Drive governance & monitoring by building tooling to monitor model drift and performance from the edge.
Hudl builds great teams and hires the best to foster continuous learning. They provide a supportive culture where employees feel valued, contributing to their recognition as a Top 100 Global Most Loved Workplace by Newsweek.
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.
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.
Manage, mentor, hire and grow 8+ ML Engineers and Data Engineers across three distinct teams
Be a strong technical partner for engineers to guide ML system architecture, model deployment, and data platform design & execution
Ensure ML solutions are production-grade, scalable, observable, cost effective and maintainable
Apella is applying computer vision and machine learning to improve the standard of care in surgery. They build applications to enable surgeons, nurses, and hospital administrators to deliver the highest quality care; they are committed to equal employment opportunity.
Design, develop, and deploy robust ML systems and multi-model AI agents that solve real-world retail challenges.
Lead the entire lifecycle, including prototyping, deployment, monitoring, and maintenance using modern CI/CD and containerisation practices.
Build high-performance data pipelines (ETL/ELT) for both training and real-time inference, ensuring our systems are scalable and reliable.
EDITED is the world’s leading AI-driven retail intelligence platform. They empower the world’s most successful brands and retailers with real-time decision making power. Their environment is dynamic and supportive, encouraging team members to take initiative, innovate, and continuously grow.
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.
Act as a solution expert across ML domains including evaluations, training, inference, data pipelines, quality, and optimisation.
Work directly alongside product teams as a trusted partner, helping them navigate technical challenges and arrive at effective solutions.
Develop blueprints, patterns, and paved roads that allow other teams to follow proven approaches and accelerate their own implementations.
Canva is a design platform that enables users to create professional designs. They have a flagship campus in Sydney, a second campus in Melbourne, and co-working spaces in other locations, with a flexible work environment.
Build Enterprise-Scale Infrastructure leveraging infrastructure-as-code to manage complex cloud environments.
Sustain Platform Health and Performance owning critical systems in production, including reliability and security.
Enable Teams and Customers to Move Faster creating abstractions and tooling that deploy, run, and scale AI/ML workloads.
Cake is on a mission to make cutting-edge AI accessible to enterprise teams. Backed by top investors, Cake is seeing strong adoption and is positioned for rapid growth in the next 12 months, emphasizing ownership, clear communication, and collaboration.
Partner closely with data engineering and data science teams to enable reliable data pipelines, analytics, and ML workflows
Support, operate, and optimize Databricks and Snowflake environments in production
Monitor, troubleshoot, and optimize systems for performance, reliability, and cost efficiency
Life360's mission is to keep people close to the ones they love with their mobile app and Tile tracking devices, empowering members to protect what they care about most with services like location sharing and crash detection. Life360 has more than 750 remote-first employees and enhances everyday family life with seamless coordination.
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.
Strong computer science or engineering background with 3+ years of coding experience with Python.
Advanced knowledge of AWS services including but not limited to their ML services (AWS SageMaker and AWS Step Functions).
Experience with ML monitoring and automation tools (MLflow, SagaMaker Pipelines).
Bluelight is a leading software consultancy dedicated to designing and developing innovative technology that enhances users' lives. With a presence across the United States and Central/South America, Bluelight is in an exciting phase of expansion, continually seeking exceptional talent to join its dynamic and diverse community.
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.
Build and operate scalable backend services and internal APIs for the AI platform.
Integrate LLMs and AI tool execution into reliable, production-ready workflows.
Own production reliability for AI platform infrastructure through observability, alerting, and incident response.
MaintainX is the world's leading Asset and Work Intelligence platform for industrial and frontline environments. They are a modern IoT-enabled cloud-based tool for reliability, safety, and operations on physical equipment and facilities, powering operational excellence for 13,000+ businesses. MaintainX recently completed a $150 million Series D round, at a valuation of $2.5 billion.
Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
Frontier Technology Inc. (FTI) delivers mission-focused solutions to the Department of Defense (DoD/DoW) and Intelligence Community (IC) through advanced engineering, digital transformation, and program execution expertise. They help their customers solve complex challenges and achieve mission success by integrating people, process, and technology.