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Brief overview of this role:
We’re looking for a Machine Learning Engineer to help us build AI products our customers will love. In this role, you’ll primarily be involved in architecting, building, training and deploying models that power our AI products. You’ll collaborate closely with product and design teams to experiment, ship fast, and bring new AI capabilities into production. This role is ideal for an engineer who enjoys end-to-end ownership, problem solving, and shaping how people interact with emerging AI technology.
Employment:
Full-time
Based: Australia
Remuneration (including Super): AUD $115,000 - $200,000
With Sitemate's diverse blend of remote and city-based team members, we ensure fair compensation regardless of your location. Our approach is informed by data sourced from Numbeo, the world’s largest cost-of-living database, which helps us maintain consistent remuneration standards across various regions.
Through our Cost of Living Multiplier, based on Numbeo's findings, we provide guidelines for adjusting salaries to reflect the cost of living in different cities. This ensures that all team members enjoy comparable living standards, no matter where they are based.
Why should I consider joining Sitemate?
Check out Sitemate’s team at the offsite: Sitemate Offsite 2022, Sitemate Offsite 2023 & recently Sitemate Offsite 2024
You can see what people really think about working at Sitemate here: Working at Sitemate
2024 Podcast: CEO Hartley Pike on his personal journey, scaling Sitemate, diversity and culture at sitemate
2023 Podcast: Listen to CMO Lance Hodgson’s tips on how to supercharge your career
2022 Podcast: Listen to CEO Hartley Pike speak about the company’s founding and broader Go To Market strategy
2022 Podcast: Listen to CTO Tim Bray speak about building high quality features that solve customer problems at Sitemate
We have equal opportunity for applicants of all genders, ethnicities, ages, sexualities & people with disabilities - Sitemate's team is proudly diverse and accessible. 55% of our team identify as coming from underrepresented ethnic backgrounds. 43% identify as female. Our team ranges in age from 22 to 51 years old.
We're proud to have a global team with team members from Australia, Kenya, Brazil, France, Chile, United Kingdom, Ireland, Spain, Portugal, Vietnam, the Philippines, Kazakhstan, USA, Canada, Indonesia, Venezuela, Sri Lanka, and Uruguay.
Our office has fantastic on-site facilities including a rooftop garden, wellness centre, showers and cycle storage. The transport connections are excellent. Every Monday, the team enjoys a catered lunch together from one of the many nearby options.
You will be remunerated based on your performance. If you are performing well, you won’t need to wait 3 years or have to find better offers elsewhere to receive an increase to your remuneration. 7 out of the last 10 remuneration increases for Sitemate team members have been made pro-actively by management, without the individual needing to even raise the topic of remuneration.
You will get a laptop and a budget to set up your home office.
You will have visibility into the standardised career development plan for your role, and access to a professional learning and development budget.
You will get additional benefits and support as parents - with paid leave for both primary (min. 16 weeks) and secondary carers (min. 6 weeks) including adoption, as well as support and leave for the tragic circumstances of stillbirth or miscarriage.
You will have 20 days PTO, as well as paid compassionate & bereavement leave, sick & carer’s leave, and the ability to work from anywhere for a few weeks per year.
You will get equity options and ownership in Sitemate on a standard vesting schedule. See here for a general overview of how this works.
Sitemate has grown from 5 employees in 2018 to 150+, and we’re moving into the next stage of growth! We have a product that customers are strongly engaged with, and every month we get 1000s of new leads in our core/target regions with 0 cold calling and limited marketing spend (made possible by strong word-of-mouth and organic marketing efforts).
Sitemate’s founders have diverse backgrounds - one coming from engineering on major infrastructure projects; the second coming from product and graphic design. This brings a unique perspective internally where we blend deep industry experience with a passion (obsession) for modern best practices in product design.
Sitemate graduated from Australia’s #1 Technology Company Accelerator - Startmate.
Sitemate has strong financial backing from Blackbird (Australia and NZ’s #1 Vc), Shearwater Growth Capital & Marbruck Investments.
See info on our latest raise here.
What is the working environment like at Sitemate?
We are very transparent - monthly Allhands meetings provide the team with direct insight into team updates, customer stories, hiring plans and key metrics (NPS, Revenue, Financial Performance). During this Allhands meeting we also hear one ‘life story’ from one of our team each month - learning about different countries and cultures around the world.
We place an extremely high value on your time - you won’t be spending your days wasting time on fighting internal systems, botched together spreadsheet processes or doing data entry.
We use best in class systems that are seamlessly integrated to deliver our team the right information at the right time, allowing them to get their work done efficiently and to a very high standard.
You will have a core and close team around you, but you will also be exposed to and working directly with multiple departments - engineering, product, design, marketing, sales and customer success.
You will have a high degree of autonomy, as the focus is on outcomes and not hours. If you want to block off a few hours to run a personal errand or block off an hour to pick up the kids from school - no worries. No one will be monitoring your time.
We are constantly working to minimise the number of group meetings - daily check ins are all done asynchronously (in your own time) each morning. But spontaneous 1:1s or small sessions are strongly encouraged!
What does Sitemate do, exactly?
Sitemate builds best in class software products for the built world.
Industrial companies operating in the built world are facing a number of mission critical challenges - changes in work behaviour as Generation Z start to enter the workforce as mobile first users of software, and the drastic shift away from traditional mining to renewable energy projects such as wind farms and solar farms.
Our first product - Dashpivot, is a platform where industrial style companies streamline and standardise their processes in a new digital format. This new digital format enables them to complete and track work in the field on a mobile or tablet, and automate repetitive tasks and tracking through automated workflows and real-time analytics.
See some of our user reviews for Dashpivot here:
G2 Crowd
Trust Radius
What will I be doing on a day-to-day basis in this role?
Initial projects will focus on intelligent document processing and conversational AI interfaces.
Own full AI generation pipelines: data prep/labeling, model selection and fine-tuning, guarded generation.
Design and ship agentic flows using core product agents and use-case orchestration.
Build and maintain data pipelines to ensure clean, reliable, and scalable training datasets.
Collaborate with product managers, designers, and engineers to integrate AI into user-facing features.
Evaluate and integrate third-party AI services and APIs to accelerate development.
Deploy, monitor, and continuously improve model performance, accuracy and costs.
Conduct experiments to evaluate new features, algorithms, architectures, or approaches.
Document workflows, share insights, and contribute to best practices in applied machine learning.
Participate in code reviews and technical discussions, sharing knowledge and shaping engineering standards.
What will be the biggest challenges for me in this role?
Navigating emerging AI technologies: Working with rapidly evolving models, APIs, and frameworks where best practices aren’t always established yet.
Building in a new domain for the company: Helping define technical foundations, architecture, and standards for AI products in an area where the company doesn’t yet have deep prior experience.
Balancing experimentation with reliability: Exploring innovative AI capabilities while still delivering production-ready software that users can trust.
Finding creative solutions for data augmentation when working with limited labeled training data.
Managing cost and latency trade-offs when scaling AI features.
Enjoys working with emerging technologies (specifically AI) and thrives in areas where the playbook is still being written.
Cares deeply about creating best-in-class user experiences, not just backend logic.
Enjoys fast-paced work, getting stuff done, and running experiments that have high impact.
Someone who can work independently with minimal ML infrastructure in place.
Comfortable being the go-to ML expert and educating the broader engineering team.
If you want to focus only on backend or only on frontend work, without touching the full stack.
Someone who’s uncomfortable with ambiguity, rapid iteration, or shaping new product directions from the ground up.
Researchers focused purely on novel algorithm development without production considerations.
Flexible hybrid work arrangements.
First ML hire with significant influence on technical direction.
Direct mentorship from engineering leadership.
Budget continuous learning.
Access to latest AI tools and platforms.
Opportunity to shape AI strategy.
Must Have:
2+ years shipping production ML/AI features.
Strong Python for ML and working knowledge of TypeScript.
Experience with LLMs (fine-tuning, prompt engineering, evaluation).
Understanding of ML fundamentals (training/validation/test splits, overfitting, metrics like precision/recall).
Hands-on experience with structured data extraction from unstructured sources.
AWS cloud experience.
Nice to Have:
Built agentic systems (e.g., LangGraph, Bedrock Agents/AgentCore, OpenAI Agents SDK) and/or MCP servers.
Experience with model quantization and optimization techniques.
MLOps: experiment tracking, model/version management, A/B or shadow testing, rollback strategies.
Experience building real-time voice features: speech recognition, conversational flow.
Experience with vector databases and RAG implementation.
Knowledge of prompt chaining and few-shot learning techniques.
Data privacy & security for AI workloads (PII handling, auditability, least-privilege IAM).
Essential Tools:
AWS Cloud.
AI/ML Platforms: OpenAI API, AWS Bedrock.
Languages: Python (focus on relevant ML libraries), TypeScript/Node.js.
Version Control: Git, experience with LFS for large files.
Modern CI/CD: Github actions, AWS CDK.
Monitoring/observability: Any ML monitoring tools and observability platform
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