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.
Due to the project requirement, candidates must be Singaporean citizens or already hold Singaporean Permanent Residency (PR) at the time of application.
- 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.
- 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.
- 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.
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.
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.
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.
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See here our AI policy.
Skills Required
- Proficient in Python or Shell for automation and task streamlining.
- Experience with ML systems using Scikit-learn, Tensorflow, MLFlow, Kubeflow, Pytorch.
- Understanding of distributed systems and scalable architectures for large-scale ML applications.
- Experience with MLOps principles and CI/CD for ML.
- Hands-on experience with cloud services for ML pipelines such as Azure, AWS, GCP, or Databricks.
- Ability to communicate effectively with stakeholders for project management.
Thoughtworks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Thoughtworks and has not been reviewed or approved by Thoughtworks.
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Leave & Time Off Breadth — Paid time off includes vacation accrual from day one with rollover, personal/sick days, development and service days, and a long‑tenure paid sabbatical. Policies also emphasize comprehensive leave for rest, illness, and rejuvenation.
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Healthcare Strength — Health coverage spans medical, dental, and vision and often extends to families, complemented by mental‑health support through Lyra and Headspace. In some regions, private providers and wellness rewards further bolster healthcare access.
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Parental & Family Support — Parental leave is positioned as generous across regions, with fully paid options in some markets and a comprehensive fertility policy. Adoption and broader family supports are also highlighted.
Thoughtworks Insights
What We Do
We are a global technology consultancy that delivers extraordinary impact by blending design, engineering and AI expertise. For 30 years, our commitment to design-led thinking, engineering excellence and innovation means we prioritize people, build teams with strong technical foundations and embed AI into every step of the process – not just as a tool but as a mindset. It’s this approach that sets us apart, sparks bold ideas and empowers us to drive real, lasting innovation. We’re not preparing for the future – we’re defining it.
Why Work With Us
As technologists, we have a unique role to play in how technology should benefit all of society, pursuing a more equitable future. Part of that role is to continuously educate ourselves on the issues that matter to the causes we believe in. We recognize our privilege and strive to see the world from the perspective of the most vulnerable.
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