• Lead the design and implementation of scalable, high-performance, and resilient ML solutions for AdTech use cases. • Architect and evolve the end-to-end machine learning pipeline – from data ingestion and training to real-time inference for our real-time bidding, targeting, and optimization algorithms. • Define the technical roadmap and vision for AI/ML in our platform, evaluating new tools and techniques (including the latest in deep learning and LLMs) and making strategic build-vs-buy decisions. • Develop intelligent systems using AI agents and agentic workflows to automate and optimize end-to-end campaign processes. • Partner with engineering, product, and data science teams to translate marketing objectives into ML-driven solutions. • Ensure system robustness and stability for ML services in a high-concurrency, low-latency environment. • Provide technical guidance and mentorship to other engineers and data scientists, fostering a culture of excellence in engineering and ML best practices.

Senior Machine Learning Engineers at Thoughtworks build, maintain and test the architecture and infrastructure for managing machine learning applications. They are involved in supporting and contributing to the design of the end-to-end applications and products. They are responsible for building core capabilities including technical and functional machine learning systems and applications, being the anchor for functional streams of work and are accountable for timely delivery. As a senior machine learning engineer, you will work on the latest tools, frameworks and offerings while also being involved in enabling credible and collaborative problem solving to execute on a strategy. Job responsibilities - You will contribute to design and drive the development of robust scalable architectures and infrastructure for deploying and managing machine learning (ML) applications, ensuring high availability, performance and security. - You will collaborate with data scientists and engineers to translate business needs into effective and efficient ML systems and applications. - You will own the development and maintenance of core functionalities within ML applications, including ML pipelines, model training and deployment, and monitoring and evaluation. - You will drive the functional stream of work by providing technical expertise, handling team discussions and ensuring timely delivery of assigned tasks. - You will stay ahead of the curve by actively exploring and implementing the latest tools, frameworks and offerings in the ML landscape. - You will facilitate collaborative problem solving within the team by actively listening, communicating effectively and mentoring other engineers. - You will contribute to the development and execution of the team's overall ML strategy, aligning technical capabilities with business objectives. - You will proactively identify and address challenges related to ML systems and applications, proposing solutions and implementing improvements. Job qualifications Technical Skills - You have experience in writing clean, maintainable and testable code, demonstrating attention to refactoring and readability of the code. - You are proficient in scripting languages such as Python or Shell for automation and task streamlining. - You have knowledge of distributed systems and scalable architectures to handle large-scale ML applications. - You have experience with building, deploying, and maintaining ML systems using relevant ML techniques and platforms, i.e.: Scikit-learn, Tensorflow, MLFlow, Kubeflow, Pytorch. - You have experience with building, deploying and maintaining ML systems and experience with application of MLOps principles and CI/CD to ML. - You have experience in machine learning engineering and data science, are familiar with key ML concepts, algorithms and frameworks, and understand ML model lifecycles. - You have experience with designing and operating the infrastructure required to run different types of ML training and serving workloads, i.e.: on-premise vs. cloud infrastructure, infrastructure as code, monitoring, etc. - You have hands-on experience with on-premise and cloud services for building and deploying ML pipelines, i.e.: Azure, AWS, GCP or Databricks and associated ML managed services. - Experience with GenAI/LLM, Prompt Engineering, AI Agents, Foundation Models, MLOps/Deployment (including CI/CD, Docker, Kubernetes, and model monitoring), Big Data Processing frameworks (Spark, PySpark). Professional Skills - You understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust along the way. - You are resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives. - You don’t shy away from risks or conflicts, instead you take them on and skillfully manage them. - You are eager to coach, mentor and motivate others and you aspire to influence teammates to take positive action and accountability for their work. - You enjoy influencing others and always advocate for technical excellence while being open to change when needed. Other things to know Learning & Development There is no one-size-fits-all career path at Thoughtworks: however you want to develop your career is entirely up to you. But we also balance autonomy with the strength of our cultivation culture. This means your career is supported by interactive tools, numerous development programs and teammates who want to help you grow. We see value in helping each other be our best and that extends to empowering our employees in their career journeys. Extra important information EEO Statement - Thoughtworks is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, ancestry, veteran status, or any other protected class. As a federal contractor, Thoughtworks is an affirmative action employer of protected veterans and individuals with disabilities. Accommodations - Thoughtworks is committed to providing reasonable accommodations to applicants with disabilities and to applicants with sincerely held religious beliefs throughout the hiring process. If you need an accommodation to apply or participate in the interview process, please complete this form or speak with your recruiter. US - Work Authorization - Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment in the United States. Responsible Use of AI in Recruitment At Thoughtworks, we use AI tools to support our recruitment team with administrative tasks such as drafting communications, scheduling interviews and writing job descriptions. Crucially, our AI tools do not screen, assess, rank or make hiring decisions. Every application is reviewed by our team and all selection decisions are made exclusively by our interviewers and hiring managers. We are committed to fairness and responsible AI. We actively manage our AI systems by testing, monitoring for biased outcomes and implementing mitigation measures. We hold our third-party vendors to these same high standards through a rigorous governance process. For additional information, please see our full Thoughtworks AI Policy for Recruitment. About Thoughtworks Thoughtworks is a dynamic and inclusive community of bright and supportive colleagues who are revolutionizing tech. As a leading technology consultancy, we’re pushing boundaries through our purposeful and impactful work. For 30+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator. Bring your brilliant expertise and commitment for continuous learning to Thoughtworks. Together, let’s be extraordinary. #LI-Remote Salary Benefits: https://www.thoughtworks.com/en-us/careers/benefits The annual salary range posted is subject to many factors and may vary depending on experience, geographic location, job responsibilities, performance, skills and/or training. Salary $130,000—$215,000 USD See here our AI policy.

Meet the Team: As a Machine Learning Engineer II – Scene Model, you will help develop and deploy machine learning models that enable autonomous trucks to understand their surrounding environment. Our team focuses on building multi-modal perception systems in bird’s-eye-view (BEV) that fuse information from LiDAR, cameras, radar, and map inputs to produce a unified representation of the scene. Working closely with teams across perception, prediction, planning, and platform infrastructure, you will contribute to models that detect objects, understand road structure, and generate spatial temporal representations used by downstream autonomy systems. This role focuses on developing and improving deep learning models, training pipelines, and data workflows that power scene understanding within the autonomy stack. What You’ll Do - Develop and train machine learning models for scene understanding, including tasks such as object detection, road and lane prediction, semantic voxel grid classification, occupancy prediction, and map understanding in bird’s-eye-view (BEV) space. - Implement production-quality ML code to support model training, evaluation, and inference within the perception stack. - Analyze model performance, identify failure modes, and propose improvements to increase robustness across diverse driving environments and conditions. - Identify and interpret objects, lanes, obstacles, and weather conditions in the driving environment. - Apply data science techniques to analyze model performance, understand data distributions, and identify corner cases. - Contribute to multi-modal perception systems, combining signals from LiDAR, cameras, radar, and map sources into unified scene representations. - Work with large-scale datasets from simulation, fleet logs, and on-vehicle data to curate training data and improve model performance. - Collaborate with data, deployment, and infrastructure teams to evaluate perception models and ensure reliable performance in real-world driving scenarios. - Help integrate perception models into the autonomy stack and testing pipelines, enabling faster experimentation and iteration. - Contribute to tooling and infrastructure that improves training efficiency, experiment tracking, and reproducibility. - Participate in technical discussions around model architectures, sensor fusion strategies, and training approaches within the team. What You’ll Need to Succeed - Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience. - Strong understanding of computer-vision, and machine learning basics. - Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments. - Strong programming skills in Python and PyTorch, with experience writing production-quality ML code. - Experience training and evaluating machine learning models using large datasets and scalable compute environments. - Understanding of ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence models. - Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines. - Ability to collaborate with cross-functional teams to integrate ML models into larger software systems. - Good technical communication skills, written and verbal. - A positive team-player mindset. Bonus Points! - PhD in machine learning or data science. - Experience working in autonomous driving, robotics, or simulation-based training environments. - Experience with distributed training frameworks or large-scale ML infrastructure (e.g., Ray, Anyscale). - Experience working with simulation environments or large-scale behavior datasets. - Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems. - Experience deploying ML models into production or real-world robotics systems. - Experience with multi-modal sensor fusion, including LiDAR, cameras, radar, or map inputs. - Experience working with BEV representations, occupancy grids, or 3D scene representations.

Meet the Team: As a Machine Learning Engineer II – Camera Models, you will help develop and deploy machine learning models that power camera-based perception for autonomous trucks. The Camera Models team builds and maintains core vision models that enable the autonomy stack to understand the environment, detect and localize objects, and estimate scene structure from camera data. Working closely with teams across perception, data, and infrastructure, you will contribute to building robust and scalable camera-based models that support safe and reliable autonomous driving in real-world freight operations. This role focuses on developing high-performance vision models and the infrastructure needed to train, evaluate, and deploy them at scale. What You’ll Do - Develop and train deep learning models for camera-based perception, enabling the autonomy stack to detect objects, understand scenes, and estimate geometric information from visual inputs. - Implement production-quality machine learning code to support model training, evaluation, and inference for camera perception systems. - Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization. - Contribute to the development and optimization of large-scale training pipelines, including dataset preparation, distributed training, and experiment management. - Work closely with data teams to curate and improve training datasets derived from fleet logs, simulation, and annotation pipelines. - Collaborate with cross-functional teams across perception, simulation, and validation to evaluate model performance and support integration into the autonomy stack. - Improve experimentation workflows and tooling to accelerate model iteration, reproducibility, and evaluation. - Contribute to discussions on model architecture, training strategies, and perception system design. What You’ll Need to Succeed - Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience. - Experience developing machine learning or deep learning models for computer vision or perception systems. - Strong programming skills in Python and PyTorch, with experience writing production-quality ML code. - Experience training and evaluating machine learning models using large datasets and scalable compute environments. - Understanding of modern deep learning architectures used in perception systems, such as CNNs, transformers, or multi-task learning models. - Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines. - Ability to collaborate with cross-functional teams to integrate ML models into larger software systems. Bonus Points! - Experience working in autonomous driving, robotics, or simulation-based training environments. - Experience with multi-task learning or perception architectures that combine multiple visual tasks. - Experience working with large-scale data pipelines, distributed training systems (e.g., Ray), or experiment management frameworks. - Familiarity with camera calibration, geometric reasoning, or 3D perception from images. - Experience deploying ML models into production or real-world robotics systems.