Link to process walkthrough video here

Thanks for stopping by and learning more about this role at Sitemate! ✨🏗️

We’d love to hear from you 👩‍💻👩🏽‍💻🧑🏿‍💻👨🏻‍💻

📝 Overview

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: Sydney, Australia or Melbourne, Australia

• Remuneration (Including Super): AUD $85,000 - $200,000 pending your experience

About Sitemate

Sitemate builds modern software for construction, infrastructure, and industrial companies - helping teams move faster, work smarter, and deliver with confidence.

Our flagship platform, Dashpivot, transforms manual, paper-based processes into simple, mobile-first digital workflows. Teams use Sitemate every day to capture data in the field, automate reporting, and make better decisions in real time.

Since launching in 2018, we’ve grown from 5 to 150+ people across 18+ countries. We’re backed by Blackbird, Australia and New Zealand’s #1 venture capital firm, and graduated from the Startmate Accelerator. Thousands of companies use Sitemate tools today - with thousands more discovering us each month through strong word-of-mouth and organic growth.

Why Join Sitemate

At Sitemate, you’ll join a global team that values transparency, high velocity, hustle, diversity, and innovation.

We’re open about our metrics, honest about challenges, and obsessed with improving how the world’s most important industries operate. Our environment is flexible, fast-moving, and outcome-driven - designed to help you do the best work of your career.

You’ll be surrounded by people who take pride in their work, care deeply about results, and believe that great culture is built on trust, respect, and continuous learning.

How We Work

• Transparency at every level: Monthly Allhands meetings share key company metrics, updates, and customer stories. We also spotlight a team member’s “Life Story” each month - it’s one of the ways we stay connected as a global team.

• High velocity, low friction: Our systems are modern and fully integrated, so you’ll spend your time solving problems - not fighting tools.

• Performance-driven growth: Seven of our last ten pay increases were made proactively, based on impact and results.

• True flexibility: We focus on outcomes, not hours. You’ll have autonomy to manage your time and work where you perform best - with no time monitoring or unnecessary meetings.

• Collaboration: You’ll work closely with other teams - engineering, product, design, marketing, and customer success - to move fast and deliver meaningful results.

What We Offer

• Competitive, performance-based remuneration

• Equity options - own a piece of what you’re helping to build

• 20 days of paid annual leave, plus sick, carer’s, and compassionate leave

• Paid parental leave - 16 weeks for primary carers, 6 weeks for secondary carers (including adoption and stillbirth support)

• Professional growth budget and a transparent career development framework

• Laptop and home-office setup budget

• Flexible work - remote or hybrid, plus the ability to work from anywhere for a few weeks each year

• Community & connection - weekly team lunches, global offsites, and our signature “Life Story” sessions

Diversity & Inclusion

We’re proud to be an equal opportunity employer. Sitemate welcomes people of all genders, ethnicities, ages, sexualities, and abilities.

55% of our team identify as coming from underrepresented ethnic backgrounds, 43% identify as female, and our team spans ages 22–51. We believe our diversity makes us stronger - and we’re committed to creating an environment where everyone can do their best work.

Learn More About Sitemate

🎧 Podcasts:

• 2024: CEO Hartley Pike on scaling Sitemate, diversity, and culture

• 2023: CMO Lance Hodgson on accelerating your career

• 2022: CTO Tim Bray on building quality features that solve real customer problems

💬 Want to know what it’s like to work at Sitemate? Hear it straight from the team: People of Sitemate
⭐ Read what our customers say: G2 Crowd, Trust Radius
🎥 Team Offsites: 2022, 2023, 2024

🔧 Day-to-Day

• 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.

⚡ Challenges

• 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.

✅ Who This Role is For

• 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.

🚫 Who This Role is Not For

• 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.

🎯 Skills & Tools

Must Have:

• 2+ years shipping production 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

PLEASE Note:

• We do not use recruitment partners or services, so please save your time and don't reach out

Compensation Range: A$85K - A$200K