About Us
icetana AI use AI to keep people safe across large scale surveillance networks. We had our strongest financial year last year, and we are on track to build on this growth and build something important and large out of Perth, Western Australia. We are looking for an AI Product Manager to help us maintain our lead as our product suite and customers expand.
The role
We're hiring an AI Product Manager (Technical) to drive the definition and delivery of our AI/analytics capabilities and the product experiences around them. Based in Perth, Western Australia, you'll work closely with our existing Product Manager (overall product strategy/roadmap) and Engineering Manager (execution and delivery). Your focus is to turn customer and operational needs into technically sound AI product requirements and to ensure that what we ship is evaluated, monitored, and improved in production.
This is a product role with serious technical depth: you won’t be writing models day-to-day, but you will be comfortable shaping how models are evaluated, deployed, and monitored, and how users build trust in the outputs.
You genuinely love AI. Not just professionally, you're the kind of person who has Claude, ChatGPT, and a half-built LLM side project open on your laptop on a Sunday. You bring that curiosity and hands-on instinct into every product decision.
What you’ll do
- Own AI feature definition end-to-end: problem framing, success metrics, data requirements, evaluation plans, rollout, and iteration.
- Define evaluation strategy and acceptance criteria:
- offline: precision/recall, ROC/PR curves, calibration, latency, robustness
- operational: false alarm rate, missed event rate, time-to-triage, operator adoption
- dataset strategy: gold sets, edge-case sets, drift detection sets
- Drive data-centric product improvements: data quality requirements, labelling workflows, feedback loops, and mechanisms to capture “ground truth” from users.
- Partner with engineering and ML engineers on architecture-level trade-offs: model packaging, inference placement (edge vs server), throughput/cost constraints, and deployment cadence.
- Own AI observability in production: monitoring for drift, performance degradation, data pipeline failures, and model/version tracking; define alerting and dashboards.
- Contribute to MLOps and release processes: model versioning, rollout/rollback, canarying, guardrails, documentation, and customer communication.
- Work with customers and internal teams to validate outcomes in real deployments (different sites, lighting, camera angles, behaviours, operational constraints).
What you’ll bring
- 3+ years shipping B2B software as a Product Manager (or similar), with meaningful AI/ML/analytics exposure.
- Strong understanding of ML product concepts:
- classification vs anomaly detection; thresholds; trade-offs
- precision/recall; class imbalance; base rates
- dataset bias, drift, and data quality constraints
- evaluation design and pitfalls (e.g., proxy labels, leakage)
- Ability to write technical product requirements that include:
- data inputs/assumptions, interfaces, performance targets
- evaluation methodology and “definition of done”
- failure modes and guardrails
- Comfort working in engineering tools and artefacts (Jira/Linear, API docs, logs, dashboards) and discussing systems constraints (latency, scale, cost).
- Excellent communication skills: you can align stakeholders and explain technical trade-offs clearly to muggles.
Nice to have
- Experience with computer vision/video analytics, real-time inference systems, or security/industrial/OT domains.
- Familiarity with edge deployment, GPU/CPU inference optimisation, or streaming architectures.
- Experience with human-in-the-loop systems (review/label/feedback workflows).
- Exposure to compliance/privacy considerations in video environments.
What success looks like (first 6–12 months)
- Clear AI feature roadmap inputs and PRDs with measurable metrics and evaluation plans.
- Improved event quality and operator trust via better tuning, explainability, and feedback loops.
- Production monitoring and model/version visibility that reduces “silent failures.”
- Faster iteration cycles from customer signal to data/eval to release.
Why icetana AI
- Real-world AI: you’ll ship products that customers rely on, not just demos.
- High ownership: influence product direction and outcomes end-to-end.
- Small team, low bureaucracy: move fast, collaborate closely, and see impact quickly.
.webp){kind=logo}
.webp){kind=logo}