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.
MEP Manager, Data Centers
Develop novel architectures, system optimizations, optimization algorithms, and data-centric optimizations that significantly improve over state-of-the-art. Take advantage of the computational infrastructure of Together to create the best open models in their class. Understand and improve the full lifecycle of building open models; release and publish insights through blogs, academic papers, etc. Collaborate with cross-functional teams to deploy models and make them available to a wider community and customer base. Stay up-to-date with the latest advancements in machine learning.
Product Security Applied AI Intern, Summer 2026
Assist in designing and implementing custom large language models (LLMs) and fine-tuning models for specific tasks. Build and experiment with agent libraries and workflow orchestration frameworks. Explore neo-cloud technologies, containerized environments, and virtualized infrastructure. Learn and apply security and privacy best practices in AI pipelines and deployments. Collaborate with the team to document, test, and optimize agent behaviors and models. Participate in knowledge sharing and mentorship sessions to gain exposure to AI, cloud, and security tradecraft.
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.
Lead Machine Learning Engineer
Set the technical direction for complex machine learning projects, balancing trade-offs and guiding team priorities. Design, implement, and maintain reliable, scalable ML and software systems while justifying key architectural decisions. Define project problems, develop roadmaps, and oversee delivery across multiple workstreams in often ill-defined, high-risk environments. Drive the development of shared resources and libraries across the organisation and guide other engineers in contributing to them. Lead hiring processes, make informed selection decisions, and mentor multiple individuals to foster team growth. Proactively develop and execute recommendations for adopting new technologies and changing ways of working to stay competitive. Act as a technical expert and coach for customers, accurately estimate large workstreams, and defend rationale to stakeholders.
Software Engineer, macOS Core Product - Palm Coast, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - New Orleans, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases; deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline; introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models; build tools to identify bottlenecks and sources of instability, then design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Seattle, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for various use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Detroit-Ann Arbor, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases; deploy and operate the core ML inference workloads for the AI Voices serving pipeline; introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models; build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Madison, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
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