Applied ML Engineer Jobs

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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.

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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.

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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.

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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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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.

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You will develop ML/AI that leverage and extend the latest state-of-the-art methods and architectures, design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios. You will be part of impactful projects and collaborate with people across several teams and backgrounds to integrate cutting edge ML/AI in production systems.

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Design and build large-scale datasets for model training. Build evaluations of speech models, both via manual annotation and at scale with automated metrics. Implement techniques for steering data generation to improve model intelligence through data and mitigate bias. Build automated quality control systems to validate and filter generated data. Partner with product teams to ensure support for key languages and markets.

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Lead the design and delivery of custom LLM solutions for enterprise customers, translating ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies. Build custom models using Cohere's foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets. Develop SOTA modeling techniques that enhance model performance for customer use-cases and contribute improvements back to the foundation-model stack, including new capabilities, tuning strategies, and evaluation frameworks. Work closely with enterprise customers to identify high-value opportunities for LLMs and provide technical leadership throughout discovery, scoping, modeling, deployment, agent workflows, and post-deployment iteration. Establish evaluation frameworks and success metrics for custom modeling engagements. Mentor engineers across distributed teams, drive clarity in ambiguous situations, build alignment, and raise engineering and modeling quality across the organization.

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As a Member of Technical Staff, Applied ML, you will work directly with enterprise customers to understand their domains, design custom LLM solutions, and deliver production-ready models that solve high-value, real-world problems. You will train and customize frontier models using Cohere’s full stack, including CPT, post-training, retrieval and agent integrations, model evaluations, and state-of-the-art modeling techniques. You will influence the capabilities of Cohere’s foundation models by developing techniques, datasets, evaluations, and insights that shape the next generation of models. Your responsibilities include contributing to the design and delivery of custom LLM solutions for enterprise customers, translating ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methods, building custom models using the foundation model stack and post-training pipelines, developing state-of-the-art modeling techniques, contributing improvements back to the foundation model stack including new capabilities and evaluation frameworks, and working as part of the customer-facing MLE team to identify high-value opportunities where LLMs can unlock transformative impact for enterprise customers.

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Design realistic and structured evaluation scenarios for LLM-based agents by creating test cases that simulate human-performed tasks and defining gold-standard behavior to compare agent actions against. Create structured test cases that simulate complex human workflows. Define gold-standard behavior and scoring logic to evaluate agent actions. Analyze agent logs, failure modes, and decision paths. Work with code repositories and test frameworks to validate scenarios. Iterate on prompts, instructions, and test cases to improve clarity and difficulty. Ensure scenarios are production-ready, easy to run, and reusable.