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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.
Job Responsibility:
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
Requirements:
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
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
Have a current right to work in Singapore i.e. Singapore Citizens and Singapore Permanent Residents only