Lead Machine Learning Engineer at Thoughtworks

, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

28 Jan, 26

Salary

0.0

Posted On

30 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Model Optimization, Runtime Optimization, Quantization, Pruning, Distillation, Deep Learning Frameworks, Performance Profiling, GPU Architecture, Inference Systems, MLOps, Benchmarking, Technical Leadership, Stakeholder Engagement, Continuous Learning, Cost Efficiency, AI Workloads

Industry

Information Technology & Services

Description

Machine Learning Engineers specializing in Inference Optimization focus on maximizing the efficiency, speed, and cost-effectiveness of deploying AI models across diverse environments. They apply advanced optimization techniques to improve runtime inference and application performance. Their work ensures that clients can scale AI solutions sustainably, whether in the cloud, on-premises, or at the edge. As a Lead Machine Learning Engineer at Thoughtworks, you’ll combine deep technical capability with team leadership and architectural thinking. You’ll guide teams through complex optimization challenges, design scalable inference systems, and ensure AI solutions are not only high-performing but operationally sustainable. You’ll act as a bridge between hands-on engineering and strategic technical direction, mentoring others while shaping the standards and practices that define excellence in inference engineering. (Tips: Thoughtworks Singapore will be shortlisting applicants who have a current right to work in Singapore i.e. Singapore Citizens and Singapore Permanent Residents only.) Job responsibilities Lead the design and implementation of advanced model optimization pipelines, including quantization, pruning, and distillation.Architect and tune inference runtimes and serving frameworks to achieve optimal performance across deployments. Guide teams in implementing high-throughput serving strategies (continuous batching, KV caching, speculative decoding, asynchronous scheduling). Develop benchmarks and performance dashboards to measure and communicate system-level efficiency improvements (throughput, latency, GPU utilization, cost). Evaluate trade-offs across accuracy, performance, and cost, and design architectures to meet target SLAs across varied hardware environments (cloud, on-prem, edge). Collaborate with infrastructure, MLOps, and product teams to embed inference optimization into production workflows and platform designs. Provide technical leadership and mentorship to engineers, fostering a culture of experimentation, rigor, and continuous performance improvement. Contribute to the development of internal frameworks, reference architectures, and playbooks for scalable and cost-efficient inference. Engage with clients to translate optimization outcomes into business value and articulate the ROI of technical improvements. Job qualifications Technical Skills Deep practical expertise in model and runtime optimization techniques (quantization, pruning, distillation, batching, caching). Proven experience optimizing inference workloads using frameworks such as vLLM, NVIDIA Triton/Dynamo. Strong proficiency in deep learning frameworks (e.g. PyTorch, TensorFlow) with production deployment experience. Ability to diagnose and optimize performance using profiling tools (e.g. Nsight, PyTorch/TensorFlow profilers). Solid understanding of GPU and accelerator architectures, and experience tuning workloads for cost and performance efficiency. Experience designing and benchmarking scalable inference systems across heterogeneous environments (GPU clusters, serverless, edge). Familiarity with observability stacks, telemetry, and cost instrumentation for AI workloads. Professional Skills Demonstrated ability to lead small-to-medium engineering teams or technical workstreams. Skilled at balancing hands-on delivery with architectural oversight and mentorship. Strong communication and stakeholder engagement skills and are able to connect low-level optimizations with business impact. Comfortable in ambiguous and fast-evolving technology landscapes, with a passion for applied innovation. Commitment to continuous learning and knowledge sharing across teams and communities. 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. 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-Onsite See here our AI policy.

Responsibilities

Lead the design and implementation of advanced model optimization pipelines and architect inference runtimes for optimal performance. Guide teams in implementing high-throughput serving strategies and collaborate with various teams to embed inference optimization into production workflows.