Intern of Technical Staff - Sovereign AI
As a Sovereign AI Intern, you will design, train and improve upon cutting-edge models to serve public interest, help develop new techniques to train and serve models safer, better, and faster, train extremely large-scale models on massive datasets, learn from experienced senior machine learning technical staff, and work closely with product teams to develop solutions.
Early Career AI/ML Engineer
As an AI/ML Engineer at Brain Co., you will design and deploy advanced large language models (LLMs) to automate complex manual processes across various sectors including healthcare, government, and energy. You will build scalable data pipelines, optimize models for performance and accuracy, prepare them for production, and monitor and maintain deployed models to ensure continuous value delivery across multiple governments worldwide. You will engage in projects such as optimizing energy production systems, modernizing government workflows, and improving patient outcomes. Interaction with government officials and collaboration with founders, AI researchers, and software engineers to address complex business challenges using AI solutions is also part of the role. Additionally, you will participate in code reviews, share knowledge, keep up with latest ML and AI developments, and uphold high-quality engineering practices, while assuming responsibilities that may include software building, product management, sales, and interpersonal skills.
Member of Technical Staff, MLE [Singapore]
As a Member of Technical Staff, Applied ML, you will work directly with enterprise customers to design and deliver custom LLM solutions that address high-value problems by rapidly understanding customer domains. You will train and customize frontier models using Cohere's foundation model stack, CPT recipes, post-training pipelines including RLVR, and data assets. Additionally, you will develop state-of-the-art modeling techniques to enhance model performance for customer use cases and contribute improvements back to the foundation-model stack, including new capabilities, tuning strategies, and evaluation frameworks. You are expected to translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies. Part of your role also includes functioning within a customer-facing MLE team to identify opportunities where LLMs can create transformative impacts for enterprise customers, while operating with early-startup ownership to set a high technical bar and define the role of Applied ML at Cohere.
Lead Software Engineer (Machine Learning)
Set the technical direction and oversee delivery of high-risk, ill-defined software and infrastructure projects, balancing strategic trade-offs and helping teams prioritize in shifting environments, taking full ownership of successful outcomes for challenging projects. Design and develop reliable, production-grade machine learning systems and justify critical architectural decisions to ensure robust delivery. Develop clear, comprehensively scoped roadmaps for novel solutions to help customers achieve strategic goals and accurately estimate effort on large workstreams for timely delivery. Engage with technical and non-technical customers at all stages of the customer lifecycle, providing reasoned and credible advice and opinions on a broad range of engineering topics. Collaborate proactively within multidisciplinary delivery teams and across the engineering community to overcome technical challenges. Coach team members on specific technologies and drive the development of shared organisational resources and libraries to streamline delivery and improve engineering methods across the company. Lead the hiring and selection process and mentor multiple individuals and managers to define the future shape of the engineering team.
Machine Learning Engineer
As a Machine Learning Engineer at Noetica, you will build ML models and pipelines with scalability and reproducibility as foundational principles, develop NLP systems that can accurately process and understand complex legal language and terminology, and design and implement LLM-based solutions that are well-documented and empower legal professionals to extract valuable insights. You will extend and create reliable model evaluation frameworks to ensure accuracy and reduce model drift or bias, simplify complex ML systems into more manageable solutions, optimize model performance through smart feature engineering and efficient algorithm selection based on actual use cases, and work with security engineers to implement responsible AI practices that protect sensitive data while delivering valuable insights.
Senior Machine Learning Engineer
Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.
Principal Machine Learning Engineer
The role involves building a platform used by Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. The candidate will work on a focused, stream-aligned, and cross-functional team that includes back-end, front-end, and design members, empowered to make its own implementation decisions towards meeting its objectives. Responsibilities include gathering and leveraging domain knowledge and experience from the Data Scientists and Simulation Engineers using the product, taking ownership of work from implementation to production, ensuring quality, scalability, and observability at every step, which includes testing, containerization, continuous integration and delivery, authentication, authorization, telemetry, observability, and monitoring.
AI / ML Solutions Engineer
The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.
Machine Learning Engineer, Applied AI
The Machine Learning Engineer is responsible for leading applied AI initiatives by bridging research and product to turn generative models into production features across the first-party app and API. Responsibilities include experimenting rapidly, building rigorous evaluations and datasets, partnering with research, engineering, infrastructure, and product teams to ship reliable and scalable ML systems. They will fine-tune and deploy models for creative use cases such as text-to-image, image-to-text, image enhancement and editing, and multimodal applications. The engineer sets clear success metrics including quality, latency, and cost, and contributes to the safety, monitoring, and reliability of the systems. They lead projects from 0 to 1 that shape Applied AI practices at Ideogram while delivering features that bring value and delight to users.
Lead Machine Learning Engineer
The Lead Machine Learning Engineer will own the development and improvement of the system predicting the next action salespeople should take to advance their relationships. Responsibilities include selecting the best model architecture and approach, involving a mixture of LLM steps and traditional ML models, picking evaluation metrics, designing systems to analyze models in production to identify areas for improvement, and identifying when to use the human data team for training or validation datasets. The engineer will read relevant research to find the best approach for their use case and, in partnership with the CTO, define how machine learning works with product engineering, model operations, and human data teams and how the team should develop moving forward.
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