Senior Machine Learning / Reinforcement Learning Engineer

Through proprietary software and AI, along with a focus on customer delight, Sleek makes the back-office easy for micro SMEs.We give Entrepreneurs time back to focus on what they love doing - growing their business and being with customers. With a surging number of Entrepreneurs globally, we are innovating in a highly lucrative space.We operate 3 business segments:Corporate Secretary: Automating the company incorporation, secretarial, filing, Nominee Director, mailroom and immigration processes via custom online robots and SleekSign. We are the market leaders in Singapore with ~5% market share of all new business incorporationsAccounting & Bookkeeping: Redefining what it means to do Accounting, Bookkeeping, Tax and Payroll thanks to our proprietary SleekBooks ledger, AI tools and exceptional customer serviceFinTech payments: Overcoming a key challenge for Entrepreneurs by offering digital banking services to new businessesSleek launched in 2017 and now has around 15,000 customers across our offices in Singapore, Hong Kong, Australia and the UK. We have around 500 staff with an intact startup mindset. We have recently raised Series B financing off the back of >70% compound annual growth in Revenue over the last 5 years. Sleek has been recognised by The Financial Times, The Straits Times, Forbes and LinkedIn as one of the fastest growing companies in Asia. Backed by world-class investors, we are on track to be one of the few cash flow positive, tech-enabled unicorns based out of Singapore.RequirementsAt Sleek, we are on a mission to streamline operations and elevate customer experience through intelligent automation powered by efficient, reliable, and production-grade ML/RL systems. We are seeking a Machine Learning / Reinforcement Learning Engineer (Applied) who will be a key individual contributor responsible for designing, building, and scaling next-generation ML/RL systems that operate under real-world business constraints.As one of Sleek’s senior applied ML/RL contributors, you will partner closely with Product, Engineering, and AI teams to translate ambiguous business problems into measurable ML/RL outcomes. You will own systems end-to-end — from model optimisation and evaluation through deployment and post-production monitoring — ensuring that ML/RL capabilities are efficient, controllable, observable, and dependable in production.You will play a central role in moving beyond generic, large-model approaches, replacing or augmenting them with small, domain-specific models, test-time reinforcement learning, and agentic systems that deliver clear improvements in quality, latency, cost, and reliability. Your work will directly shape how ML/RL is deployed across Sleek’s products and internal operations.You Will EnsureEfficient, production-ready ML/RL systems that make explicit, data-driven trade-offs between quality, latency, throughput, and costRobust optimisation and evaluation practices, including benchmarks, regression testing, and production monitoring, to ensure sustained performance over timeReliable test-time reinforcement learning and agentic workflows, with guardrails, fallbacks, and observability to manage risk and instabilityPragmatic integration of ML/RL into real systems, designed for scalability, maintainability, and operational excellence rather than experimentation aloneClear technical communication and cross-team alignment, enabling predictable delivery and informed decision-makingA high bar for engineering discipline, including reproducibility, monitoring, documentation, and continuous improvement Key outcomes in the first 6-12 months Ship High-Impact ML/RL SystemsDeliver production-grade ML/RL systems that create measurable improvements in quality, latency, cost, or reliability.Replace or augment baseline approaches with small, domain-specific models where they provide superior performance-to-cost trade-offs.Define and track clear success metrics and benchmarks for all deployed systems.Establish Efficient Model Training & Serving (SMOL)Build and operate efficient training and serving pipelines using distillation, quantization, and parameter-efficient fine-tuning.Maintain benchmark suites covering quality, latency, throughput, memory, and cost.Drive explicit, data-backed trade-offs in model and deployment decisions.Deploy Test-Time RL & OptimizationImplement test-time optimisation (TTRL / TPO) to improve generative or agentic outputs within strict latency and cost budgets.Introduce reward-guided decoding or reranking with measurable gains.Add monitoring, guardrails, and fallback strategies to manage instability and regressions.Build Reliable Agentic SystemsDesign and ship agentic workflows with multi-step planning and execution across tools and data sources.Implement orchestration for long-running workflows (state, retries, timeouts, idempotency).Establish evaluation harnesses and regression tests to track agent reliability and cost over time.Establish ML/RL Operational ExcellenceIm