Build scalable Edge infrastructure, designing, developing, and maintaining delivery systems to deploy models to fleets of devices.
Work with cross-functional teams, collaborating with Data Scientists, Embedded Engineers and Product Managers to ensure smooth integration of complex features and capabilities.
Drive automation and reliability, implementing infrastructure to silently test candidate models on production devices, and build telemetry pipelines to monitor drift.
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.
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).
AUTO1 Group Technology drives innovation in the used car market across Europe. They operate at the intersection of software engineering, data science, and DevOps, helping bring state-of-the-art ML models—such as large-scale recommendation systems and transformer-based neural networks—safely into production.
Design and implement MLOps pipelines to automate model training, deployment, monitoring, and management
Lead/mentor a team of MLOps Engineers, fostering an inclusive and collaborative environment that encourages innovation and continuous learning
Collaborate with Data Scientists and ML Engineers to ensure models are production-ready, scalable, and maintainable
Egen is a fast-growing and entrepreneurial company with a data-first mindset. They bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights.
Guide the technical direction of Bondora’s ML engineering stack by selecting, evaluating, and implementing technologies to improve scalability and reliability.
Lead complex, high-risk, or cross-departmental projects that directly influence Data Science delivery, risk model performance, and production stability.
Act as the bridge between Data Science, Data Engineering, and Development to identify and solve systemic technical challenges.
Bondora's mission is to empower people to enjoy life more while alleviating the stress of managing finances. Founded in 2008, Bondora has served over 1 million customers for 16 years and is rapidly growing as a fintech company, set to acquire a banking license and expand investment and loan products across Europe.
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.
Build and drive roadmap for the Autonomy Data Flywheel
Draft requirements and ensure delivery for all consumers of the data including End-End AI, Perception, Safety and other teams.
Work with engineering teams to set and achieve targets for both quality and quantity KPIs
Serve Robotics is reimagining how things move in cities with its sidewalk robot designed for deliveries. The company aims to make deliveries accessible and benefit local businesses, with a team of tech industry veterans in software, hardware, and design.
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.
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.
Design, implement, and maintain high-performance ML training and inference platforms.
Ship tools that allow any ML engineer to deploy a model in minutes, not days.
Improve scalability, reliability, and cost efficiency of model training and serving systems.
Speechify's mission is to make sure that reading is never a barrier to learning. With nearly 200 people around the globe working in a 100% distributed setting, Speechify's team includes frontend and backend engineers, AI research scientists, and others.