About Respondent
Respondent is building the infrastructure that powers participant recruiting for the research industry. Our mission is simple but ambitious: to make it effortless for researchers to find high-quality human participants - whether they're testing a product, exploring a market, or validating an idea.
From our founder's first recruits on a NYC subway to a global panel of 4 million participants across 150+ countries, we now support research for companies like Microsoft, Dropbox, and P&G. Our panel is one of the most diverse and responsive in the industry.
We're a 35-person team across Sydney, Manila, and the US. We're intentional about collaborating in person - serendipitous hallway conversations, whiteboarding sessions, and quick alignment cycles help us move faster and build something great together.
We've gone from 0 to 1, recently reaching profitability and intending to stay so. We're scaling with intention and reinvesting in the business to grow our platform, panel, and team. Joining at this stage means your work will have a direct impact as we take Respondent from 1 to 10.
The Role
We're seeking an Applied Machine Learning Research Engineer to work on-site with our Product, Data, and Engineering teams to design, prototype, and productionise novel machine learning models.
This role is focused on experimental ML systems that require rapid iteration, close collaboration, and hands-on access to internal data and infrastructure. You'll be expected to move quickly from idea → prototype → evaluation → production.
What You'll Do
Research and develop custom ML models for prediction and classification related to participant behaviour and engagement
Build and maintain production-grade ML pipelines (training, evaluation, deployment, monitoring)
Run controlled experiments and offline evaluations to validate impact before rollout
Collaborate closely with in-office Product, Data, and Engineering stakeholders to ship improvements end-to-end
Produce clear technical documentation to support model understanding, maintenance, and iteration
What You'll Bring
Master's or PhD in Machine Learning, Computer Science, Statistics, or a related field
Strong applied ML experience (taking models from research → real-world deployment)
Proficiency in Python and modern ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
Strong experimental mindset: you can evaluate tradeoffs, measure outcomes, and iterate fast
Why Join Us?
Real impact: You'll be part of a small, high-ownership engineering team where your work ships fast and directly shapes the experience for customers like Microsoft and Dropbox.
Profitable and growing: We're a 30-person startup that's scaling - you'll get the excitement of startup growth with the stability of proven traction.
Modern tech and mindset: Work with the latest tools and AI-powered dev practices, giving you a front-row seat to cutting-edge engineering.
Culture that cares: Be part of a tight-knit global team (Sydney, Manila, US). We're collaborative, supportive, and believe in balancing high performance with a fun workplace.