Machine Learning Engineer
Design, build, and maintain scalable machine learning systems including data ingestion, preprocessing, training, testing, and deployment. Develop and optimize end-to-end ML pipelines encompassing data collection, labeling, training, validation, and monitoring to ensure reliability and reproducibility. Implement robust MLOps practices such as model versioning, experiment tracking, CI/CD for machine learning, and continuous monitoring in production environments. Collaborate with product and engineering teams to integrate and deploy models into real-time products with a focus on efficiency and scalability. Ensure data quality, observability, and performance across all AI systems. Stay current with the latest AI infrastructure, tooling, and research to support ongoing innovation.
AI/ML Engineer
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
Machine Learning Engineer
As a Data Scientist (Algorithm Engineer) in Delivery, you will work closely with Simulation Engineers, Machine Learning Engineers, and customers to understand and define engineering and physics challenges, while providing technical leadership to your team. Your responsibilities include leading the pre-processing and analysis of complex data to prepare it for predictive modelling, establishing best practices and methodologies for your team, architecting and developing innovative deep learning models combined with optimisation methods to predict and control physical systems, and taking full responsibility for the quality, accuracy, and impact of your work and your team's work. You will design, build, and test data pipelines that are reliable, scalable, and easily deployable in production environments, lead cross-functional collaboration to ensure model integration with simulations, drive internal research and product development, mentor junior team members, lead communication and presentations with technical teams and customers, and represent the company as a technical authority when visiting customer sites globally. Additionally, as a senior member, you will influence technical direction and shape future solutions and products while developing leadership skills.
ML Engineer, Post-Training and Evaluation
As a ML Engineer on Reflection's Applied AI team, you will fine-tune Reflection's open-weight models for specific customer use cases by preparing datasets, configuring training runs including SFT, preference optimization, and reinforcement fine-tuning, and iterating based on evaluations. You will build and maintain evaluation infrastructure by designing eval suites, curating test sets, establishing baselines, and measuring model improvements. You will prepare training data from raw customer inputs by inspecting data quality, cleaning and formatting datasets, identifying adversarial or noisy samples, and building reproducible data pipelines. You will debug and diagnose training and inference issues by interpreting loss curves, catching data quality problems, and identifying training dynamics issues. Additionally, you will support end-to-end deployments of fine-tuned models across hybrid environments such as public cloud, VPC, and on-premises, ensuring inference performance and reliability in production. You will also contribute to evolving playbooks, evaluation benchmarks, and best practices within the fine-tuning and evaluations practice.
Member of Engineering (Post-training)
Research and experiment on ways to specialize foundational models to agentic use cases, build and maintain data and training pipelines, keep up with latest research and be familiar with state of the art in LLMs, alignment, synthetic data generation, and code generation, design, analyze, and iterate on training, fine-tuning, and data generation experiments, write high-quality and pragmatic code, and work as part of a team by planning future steps, discussing, and communicating clearly with peers.
Member of Technical Staff - ML Performance
The role involves engineering work focused on making machine learning systems performant at scale. This includes contributing to open-source projects and enhancing Modal's container runtime to improve the throughput and reduce the latency of language and diffusion models.
AI/ML Engineer, Rome
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
Access all 4,256 remote & onsite AI jobs.
Frequently Asked Questions
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.