Senior Machine Learning / Reinforcement Learning Engineer at Sleek Property LLC

, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

06 May, 26

Salary

0.0

Posted On

05 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Reinforcement Learning, Python, PyTorch, Model Optimisation, Evaluation, Test-Time Optimisation, Agentic Systems, Production Monitoring, Collaboration, Documentation, Continuous Improvement, Data-Driven Decision Making, Scalability, Maintainability, Operational Excellence

Industry

Accounting

Description

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. 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. You Will Ensure Efficient, production-ready ML/RL systems that make explicit, data-driven trade-offs between quality, latency, throughput, and cost Robust optimisation and evaluation practices, including benchmarks, regression testing, and production monitoring, to ensure sustained performance over time Reliable test-time reinforcement learning and agentic workflows, with guardrails, fallbacks, and observability to manage risk and instability Pragmatic integration of ML/RL into real systems, designed for scalability, maintainability, and operational excellence rather than experimentation alone Clear technical communication and cross-team alignment, enabling predictable delivery and informed decision-making A 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 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. 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 & 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. 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. 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 Candidates must demonstrate hands-on, production experience across all areas below. 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. 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. Case Study A ~90 minute live test Behavioural / Soft Skills fit assessment A ~45 minute chat with a leadership team, 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. Some other great things about working at Sleek… Humility and kindness: Humility is a core attribute we hire for, which means we have a culture of not taking ourselves too seriously and being able to laugh. Kindness is also incredibly important. We are committed to creating and nurturing a diverse and inclusive environment. Flexibility: If you need to start early or start late to cater to your family or other needs, we don’t mind, so long as you get your work done and proactively communicate. You can also work fully remote from anywhere in the world for 1 month each year Financial benefits: We pay competitive market salaries and provide staff with generous paid time off and holiday schedules. Certain staff at Sleek are also eligible for our employee share ownership plan and can share in the upside of our stellar growth trajectory as we work toward listing on a prominent stock exchange in the Asia Pacific region. Personal growth: You’ll get a lot of responsibility and autonomy at Sleek - we move at a fast pace so you’ll be making decisions, making mistakes and learning. There’s also a range of internal and external facing training programmes we run. We’re also at the forefront of utilising AI in our space and are developing a regional centre of AI excellence. It is our intention that if you leave Sleek, you leave as a more well-rounded person and professional. Sleek is also a proudly certified B Corp. Since we started our journey in 2017, we’ve been committed to building Sleek as a force for good. In just over 5 years, we’ve joined a community of industry leaders like Patagonia, Ben & Jerry's, and P&G who are building an inclusive, equitable, and a regenerative economy. We have planted over 29,271 trees to reforest our ecosystem and saved 7 tons of paper from landfills by processing over 1.4M pages through SleekSign. We aim to be Carbon Neutral by 2030.

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Responsibilities

The role involves designing, building, and scaling ML/RL systems that operate under real-world business constraints. The engineer will partner with various teams to translate business problems into measurable ML/RL outcomes and ensure the systems are efficient and reliable in production.