Senior Machine Learning / Reinforcement Learning Engineer position at Sleek.| Australia

_{2026-02-05T04:35:36.816Z} bot

Sleek Senior Machine Learning / Reinforcement Learning Engineer

Experience: 5-years

Pattern: Full-time

Country:

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Australia

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 incorporations Accounting & Bookkeeping : Redefining what it means to do Accounting, Bookkeeping, Tax and Payroll thanks to our proprietary SleekBooks ledger, AI tools and exceptional customer service FinTech payments: Overcoming a key challenge for Entrepreneurs by offering digital banking services to new businesses Sleek 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. Mission: At 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. Key outcomes in the first 6-12 months 1. Ship High-Impact ML/RL Systems Deliver 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. 2. 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. 3. Deploy Test-Time RL & Optimization Implement 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. 4. Build Reliable Agentic Systems Design 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. 5. Establish ML/RL Operational Excellence Implement production monitoring for quality, latency, cost, and failure modes. Ensure training, experimentation, and deployment are reproducible, documented, and observable. Partner closely with Product and Engineering to translate ambiguous problems into shippable ML/RL solutions. Must-have experience Applied ML in Production : ~5+ years building, training, and shipping ML systems using Python and PyTorch, with clear ownership beyond experimentation. Efficient Model Training (SMOL): Experience replacing or augmenting large models with smaller, domain-specific ones using distillation, quantization, or parameter-efficient fine-tuning, supported by clear benchmarks. Reinforcement Learning & Test-Time Optimization: Solid RL fundamentals and experience deploying inference-time optimisation systems (e.g. reward-guided decoding, reranking) under latency and cost constraints. Agentic Systems: Experience building multi-step agents with orchestration concerns such as state, retries, timeouts, and fallbacks, and improving their reliability and cost in production. ML/RL Operational Excellence: Experience with reproducible training pipelines, evaluation, monitoring, and production debugging, and collaborating closely with Product and Engineering on constraint-driven problems. For applicants based in Singapore and Australia, this will be a hybrid role. For applicants based in India, Vietnam, and the Philippines, this will be a fully remote role. Behavioural fit is also important at Sleek, and we will be looking for candidates that have a proven track record of embodying the below attributes in their recent roles: Ownership : This shows reliability and helps build trust within the team. We move fast and need to know that everyone will see things through to completion and proactively help to get things back on track when challenges arise. Accountability is really important to us. Humility : There is so much we don’t know. Humility allows for open-mindedness to feedback and a willingness to learn from others. It paves the way for collaboration and creates a positive work environment. It is a key ingredient of self awareness and emotional intelligence. Structured Thinking : Our business is complex with many layers (many services, many countries, many cultures). Regardless of whether you’re more analytical or creative in nature, being able to show sound judgement is important to us. It ensures solutions are pragmatic and balance the needs of the organisation, team and customers. Data driven : We are a data rich business with ~15,000 small customers. Each decision we make can impact many more people than we realise - so it’s critical that we use sound data to support our strategies and review the success of our initiatives. Can have tough conversations in a positive way : It’s not a matter of if, but when difficult interpersonal situations arise. Disagreement, conflict and disappointment are a given in a fast moving business where people care about their work. People that proactively have tough conversations with kindness build empathy, trust and great working relationships. The interview process The successful candidate will participate in the below interview stages. We anticipate the process to last no more than 3 weeks from start to finish. Whether the interviews are held over video call or in person will depend on your location and the role. TA Screening A ~30 minute chat with the Talent Acquisition Team. Take Home Task Shortlisted candidates will be asked to complete a take home task. Technical Round A ~45 minute interview with the hiring manager Behavioural / Soft Skills fit assessment A ~45 minute chat with our CTO, where they will dive into some of your recent work situations to understand how you think and work. Offer + reference interviews We’ll make a non-binding offer verbally or over email, followed by a couple of short phone or video calls with references that you provide to us. +++++ Requirement for background screening Please be aware that Sleek is a regulated entity and as such is required to perform different levels of background checks on staff depending on their role. This may include using external vendors to verify the below: Your education Any criminal history Any political exposure Any bankruptcy or adverse credit history We will ask for your consent before conducting these checks. Depending on your role at Sleek, an adverse result on one of these checks may prohibit you from passing probation. By submitting a job application, you confirm that you have read and agree to our Data Privacy Statement for Candidates, found at sleek.com.

Senior Machine Learning / Reinforcement Learning Engineer job opportunity at Sleek.

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