AI Product Manager

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 600 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 Asia Pacific.

At Sleek, we are on a mission to streamline operations and elevate customer experience through intelligent automation and modern engineering.

About SleekOne and this role

SleekOne is Sleek's AI-native accounting platform: a deterministic ledger wrapped in a layer of AI agents that collect, read, classify, match and post the financial documents of thousands of small businesses, with accountants reviewing exceptions rather than keying transactions. It is the single most consequential product bet in the company, owing to the fact that it replaces third-party ledger and extraction tooling across four markets and sets the operating leverage of our entire accounting business.

Mission: Own the document-to-ledger pipeline end to end, from the moment a receipt, invoice or bank statement enters the system to the moment a reconciled, correctly categorised transaction is posted, and raise its straight-through rate while holding precision at the standard a regulated accounting firm requires.

We are looking for an AI Product Manager who treats model quality as a product problem rather than a research curiosity. This person owns the extraction, quality-checking, matching and reconciliation agents at the heart of SleekOne, works daily with ML engineers, backend engineers and practising accountants, and is accountable for measurable automation outcomes rather than shipped features. This is a hands-on role in a lean setup with real ownership over what gets built, how it is evaluated, and how it performs in production.

Primary accountability

You own the four stages of the pipeline that turns customer documents into a closed set of books, and the metrics that govern each stage.

• #1: Document extraction. Own the accuracy and coverage of structured data pulled from invoices, receipts, bank statements and supporting documents across formats, languages, currencies and scan quality in Singapore, Hong Kong, Australia and the UK. Define the extraction schema per document type, decide which fields must be correct for a transaction to be auto-posted, and run the evaluation harness that benchmarks each model release against a labelled production set and a frontier baseline.
• #2: Quality checking. Own the classification and confidence layer that decides whether an extracted document is trusted, routed to an accountant, or sent back to the customer. Design the scoring and threshold logic so that the system auto-publishes aggressively where it is right and declines where it is not, and build the feedback loop that converts accountant corrections into training signal and threshold adjustments.
• #3: Matching with bank statements. Own the logic that pairs documents to bank-statement lines, including one-to-many and many-to-one matches, partial payments, foreign-currency settlements, bank fees and timing differences. Define the match-confidence model, the handling of unmatched items on both sides, and the customer and accountant experience for resolving them.
• #4: Reconciliation. Own straight-through reconciliation to the ledger: categorisation, posting, and the month-end close for each customer. Measure and move the reconciliation rate, precision of auto-posted entries, rework rate and on-time close, and own the exception queue that accountants work from when the system cannot decide.

Key outcomes

• Automation rate materially higher, precision intact: A rising share of customers whose documents flow from upload to posted, reconciled ledger entry with no human touch, with precision on auto-posted and auto-reconciled entries held above 99% and rework below 1%.
• An evaluation discipline the company trusts: Every extraction, classification and matching model release is benchmarked on a labelled production dataset against a defined floor and a frontier baseline before it ships; regressions are caught in evaluation, not by accountants.
• A closed learning loop from accountant corrections: Corrections made during review feed back into categorisation rules, confidence thresholds and training data on a defined cadence, so that the same error class is not corrected twice.
• Exception volume shrinking in both count and cost: Unmatched items, low-confidence documents and failed reconciliations are triaged into named error classes with owners, and the accountant time per exception falls quarter on quarter.
• Engineering and accounting teams aligned on the problem: ML and backend engineers report clear problem statements, acceptance criteria expressed as metric thresholds, and well-specified edge cases; accountants report that the system fails predictably and explains itself.

What you will do

• Define the roadmap for the extraction, quality, matching and reconciliation agents, sequenced by automation impact per unit of engineering effort and by the precision risk each change introduces.
• Write specifications that read as evaluation criteria: for each change, the dataset it will be judged on, the metric floor it must clear, and the failure modes it must not reintroduce.
• Build and maintain labelled evaluation sets with the accounting team, including deliberately hard cases such as multi-page statements, handwritten receipts, foreign-currency invoices and split payments.
• Analyse production failures weekly, classify them by root cause (model, prompt, schema, upstream data, customer behaviour), and decide which classes are worth engineering effort and which are better handled by workflow or customer communication.
• Design the confidence, threshold and routing logic that governs when the system acts alone, when it asks an accountant, and when it asks the customer.
• Partner with ML engineers on model, prompt and cascade choices, including when to use a specialised model, a frontier model, deterministic rules or a combination, with cost per document as an explicit constraint.
• Work alongside bookkeepers and accountants in Singapore, Hong Kong, Australia and the UK to understand how reconciliation is performed by hand, where the system's output differs from theirs, and which market-specific conventions must be encoded.
• Instrument the pipeline so that automation rate, precision, exception volume and cost per document are visible by market, document type and customer segment, and report them to product and finance leadership.
• Own the accountant-facing review and exception-handling experience, and the customer-facing prompts that resolve missing or ambiguous documents.

Why this role matters

Every percentage point of straight-through automation in this pipeline frees accountant capacity across four markets, and that capacity is the mechanism by which Sleek converts revenue growth into margin. The person in this role will have direct, measurable influence on the operating leverage of the accounting business and on the trust several thousand small-business owners place in their books. The work is close to the core of the product and to the economics of the company, with the opportunity to shape SleekOne as it scales from migration to platform.

Requirements

Requirements

• 5+ years building digital products, including at least 2 years as the product owner of a machine-learning or LLM-based feature in production, accountable for its quality metrics rather than only its delivery.
• Demonstrated experience with document understanding, OCR, information extraction, entity matching, or a comparable structured-data-from-unstructured-input problem.
• Working fluency with precision, recall, confidence calibration, evaluation datasets, error analysis and the trade-off between automation coverage and accuracy; you can read a confusion matrix and act on it.
• Hands-on comfort with data: SQL and enough Python or spreadsheet modelling to query production data, build an evaluation set and size an opportunity without waiting for an analyst.
• Track record of shipping and iterating on products used by real customers, in close partnership with engineering in agile or lean teams.
• Clear written communicator who can express a product decision as a metric threshold, a decision rule or a data contract that engineers and accountants read the same way.

Role-specific attributes

• Treats model output as a product surface: understands that a 93% extraction benchmark and a 99% production floor are different problems requiring different architecture.
• Understands, or can rapidly learn, double-entry bookkeeping, bank reconciliation and month-end close well enough to argue with an accountant about an edge case.
• Comfortable with LLM-era tooling: prompt and schema design, structured outputs, retrieval, agent orchestration, and cost and latency budgeting per document.
• Operates with limited structure and high ambiguity, with a bias toward instrumenting first and deciding from evidence.
• Proven ownership of product outcomes, not deliverables.

Nice to have

• Background in accounting, fintech, or financial operations; experience with bank feeds, open banking data, or ledger systems.
• Prior exposure to multi-market document handling across Singapore, Hong Kong, Australia or the UK, or to regulated-industry quality controls.
• Experience with human-in-the-loop review systems, active learning, or labelling operations.
• Background in data science, ML engineering, or software engineering before moving into product.
• Experience in early-stage or fast-moving product teams.

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.

Attention to detail: You’ll be managing multiple, complex workstreams from numerous stakeholders. It will be important for you to keep track of everything and notice when information is missing or inconsistent.

Excellent listener and clear communicator: We have a variety of nationalities and for many people, English isn’t their first language. For you to excel, you’ll need to be present in your calls and make sure you properly receive and thoughtfully send messages to others in the business.

About the interview process

The successful candidate will participate in the below interview stages (note that the order might be different to what you read below).

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.

A ~30 minute chat with our Talent Acquisition team to learn more about you, your background, and what you’re looking for.
A ~60 minute technical interview with the Product Team
A ~60 minute AI skills interview with the AI Lead.

A final interview with our CPO, giving you the opportunity to ask any final questions and hear more about our vision and priorities.

Reference verification
Prior to final offer confirmation, we will conduct one professional reference check with your recent direct manager or supervisor. To keep the process moving smoothly, this step may be initiated during the final interview stages.

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.

Benefits

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: The role will be fully remote, with work from home five days a week However, if you are based in Singapore, the role will follow a hybrid model. 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.