Applied ML Engineer Jobs

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Design, develop, and deploy AI/ML models ranging from traditional ML regression algorithms to transformer-based architectures. Train, fine-tune, and optimize deep learning and LLM-based solutions. Engage with customers to understand their needs and transform them into actionable tasks for developing new functionalities within the Articul8 platform. Collaborate with researchers, software engineers, and product teams to integrate new AI capabilities into applications. Implement and evaluate state-of-the-art automated testing and metrics to improve model accuracy and efficiency. Optimize models for both cloud and on-premises environments to ensure low latency and high availability. Develop APIs and micro-services to serve AI models in production. Stay current with the latest AI models, research, and best practices. Ensure ethical AI practices, data privacy, and security compliance.

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Design, train, and fine-tune state-of-the-art language models; develop AI agents combined with retrieval-augmented language models; build efficient and scalable machine learning training and inference systems; stay up-to-date with the latest literature and emerging technologies to solve novel problems; work closely with product and design teams to develop intuitive applications that create societal impact.

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The Senior AI/ML Engineer is responsible for designing and implementing autonomous agents capable of task decomposition, reasoning, and self-correction, building systems that enable complex multi-step agentic workflows. They develop robust interfaces for large language models (LLMs) to interact with external APIs, databases, and financial tools, ensuring reliable function calling and accuracy within the spend-to-pay ecosystem. They lead the integration of advanced LLMs, focusing on Retrieval-Augmented Generation (RAG) and long-term memory management for high-stakes financial decision-making. Additionally, they architect and manage MLOps pipelines including continuous integration, continuous delivery (CI/CD), model serving, monitoring, and automated retraining to ensure the reliability, scalability, and efficiency of ML services. They also collaborate cross-functionally with product managers, software engineers, and data scientists to translate business requirements into technical solutions and integrate AI/ML models into core platforms.

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The Head of Machine Learning will manage 9 Machine Learning Engineers, including 3 Team Leaders, with responsibilities spanning People Management and project coordination. They will understand and coordinate the strategic direction of ML team projects, manage dependencies, allocate resources, and ensure alignment with business and product goals. This includes contributing to system architecture and development by empowering the team via 1:1s, code reviews, and discussions to deliver impactful features. The role involves leading and nurturing the ML engineering team through coaching and mentorship, leading team OKR discussions, coordinating projects, facilitating meetings, and collaborating with the CTO, Platform, and Product Managers to align team priorities with company OKRs. They will work with the People team on recruiting and onboarding talent, act as a sounding board for the team, support identifying and resolving bottlenecks and blockers to enable faster iteration, drive ML system development and deployment, optimize tools and infrastructure for efficiency, and promote a culture of collaboration and continuous learning while mentoring team members.

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As an engineer in the Offline Driving Intelligence (ODIN) team at Zoox, the responsibilities include developing advanced multimodal large language models to enhance environmental understanding for robotaxis, designing model architectures and training techniques using sensor inputs and large scale data, driving end-to-end machine learning solutions from research to production using Zoox's data pipelines and infrastructure, collaborating with perception, planning, safety, and systems teams to integrate models into the vehicle's decision-making pipeline, and validating and optimizing solutions using real-world driving scenarios to contribute directly to the safety and reliability of Zoox's autonomous system.

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Contribute to tooling for AI-based scenario understanding and validation. Synthesize realistic autonomous vehicle simulation scenarios with dynamic (e.g., traffic) and static features. Integrate and validate large language models (LLMs), vision-language models (VLMs), and implement other models for complex scenario generation workflows, leveraging techniques like agentic tool use. Collaborate directly with internal customers and partner teams to provide generative AI solutions for their test creation workflows. Directly contribute to the safety and reliability of Zoox's autonomous software.

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As a software engineer on the perception mapping team, you will curate, validate, and label datasets for model training and validation. You will research, implement, and train machine learning models to perform semantic map element detection. You will closely collaborate with validation teams to formulate and execute model validation pipelines and integrate models into the greater onboard autonomy system within compute budgets. Additionally, you will act as a technical leader on the team, maintaining coding and ML development best practices and contributing to architectural decisions.

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Design, build, and maintain software supporting the introspection of machine learning-based motion planners. Develop introspection techniques and provide autonomy software engineers tooling used to understand and debug motion planning behavior. Lead new initiatives to introspect the output of machine learning motion planning models by utilizing existing cutting edge techniques as well as developing novel introspection techniques. Design new architecture and implement new tools used to analyze machine learning model behavior. Collaborate with engineers on Perception, Prediction, Planning, Machine Learning Model, and Visualization teams to enable development of behavior improvements.

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As a Senior Machine Learning Engineer for ML Agents and Planning, you will develop new deep learning models using imitation learning and reinforcement learning to generate driving plans for human-like agents. You will work on novel techniques to estimate the quality of driving plans in terms of safety, progress, comfort, and realism. You will contribute to large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field. Additionally, you will develop metrics and tools to analyze errors and understand improvements of the systems. Collaboration with engineers on Perception, Planning, Simulation, and Validation teams will be essential to address the overall Autonomous Driving problem.