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Full-time
Equity, Flexible working arrangements
Posted 23h ago
~40 hrs/week
Responsibilities
You will own the AI layer's learning loop, including building evaluation systems and optimizing retrieval over messy fleet data. Additionally, you will drive inference cost reduction and train task-specific models to improve performance at scale.
Requirements
Candidates should have at least 3 years of experience in applied ML with practical knowledge of LLM systems and Python development. A pragmatic, measurement-driven approach to shipping production-ready code is essential for this role.
Full job description
About Alloy Robotics
We believe robotics is the industry of our lifetime, and data is its lifeblood.
Alloy is the AI-native data platform for robotics teams. Robot fleets generate enormous volumes of logs, video, telemetry, and mission data, and most of it ends up in silos where nobody can use it. Alloy turns it into one queryable layer with AI agents on top: engineers ask questions in natural language, get automated mission reports, and catch failures before they compound. The point is simple. Your robot breaks, you find out why, and your best people spend their time building the next robot instead of digging through the last one.
We've raised $16m, backed by Square Peg, Blackbird, and Airtree, with angels including senior leaders from OpenAI, Anthropic, Tesla, Waymo, and Halter. We partner with robotics teams across defence, agriculture, maritime, humanoids, construction, and medical, including Advanced Navigation, DroneForge, Breaker Industries, and Puralink. We're a lean team building from Sydney, and are adding our first US hires.
Where we are, and where we're heading
Alloy sits on data almost nobody has: real operations from real robot fleets, flowing through a product engineers use every day. Your job is the loop that turns that into compounding advantage: agents that get smarter with every fleet we serve, at a cost that keeps falling.
About the role
You'll own the AI layer's learning loop: evaluations we trust, retrieval that works on messy fleet data, model and harness choices that bend the cost curve. This is applied ML shipped weekly, not a research seat.
What you'll do
Build the evaluation systems for quality, speed, and cost
Turn cross-fleet usage into datasets and detectors that improve with scale
You may be a good fit if you have
3+ years in applied ML
Real LLM-systems experience: evals, retrieval, fine-tuning
Python that ships product, not notebooks
A pragmatic, measurement-driven style
Strong candidates may also have
Models you trained yourself: embeddings, fine-tunes, task models
Time-series or sensor-data experience
Inference cost optimisation at scale
Open-source work we can read
How we work
We're building the company we always wanted to join: small, fast, and serious about the craft. The bar is work you'll be proud of in five years, shipped this week. Everyone owns outcomes end to end: the interesting problems don't come with instructions, and nobody will hand you a playbook. We question requirements, delete what shouldn't exist, and spend our energy where customers feel it. If you want clear lanes and a manager to set your week, we'll frustrate you. If you want real problems, real pace, and the best work of your career, you'll fit right in.
Compensation
Competitive salary
Additional compensation and benefits may include equity and flexible working arrangements
If this reads like you but you don't tick every box, apply anyway. We care more about trajectory than checklists.
Related keywords
ML EngineerRoboticsAILLMPythonMachine LearningData PlatformFleet DataInferenceDistillationEmbeddingsFine-tuningTime-seriesSensor-dataEvaluation SystemsRetrieval
Your robot breaks, find out why. Built for frontier hardware teams.
Industry
Robotics Engineering
Company size
11-50 employees
Founded
2025
Headquarters
Surry Hills, New South Wales
LinkedIn followers
1,645
Alloy builds agents for modern robotics teams.
Stream data off your robots, store it at a fraction of the cost, and analyse it in plain English.
The problem: Most teams tape together S3, Jupyter, Foxglove, and a pile of scripts. Then spend weeks maintaining it instead of building their product.
Alloy replaces that stack:
1. Capture: edge agent streams data off your robots automatically, no silent failures.
2. Store: robotics-native storage, no egress fees, cheaper than S3.
3. Understand: chat with your fleet data in plain English, find root causes in minutes.
4. Track: monitors every mission for regressions, so every deployment improves your robot.
5. Integrate: pipes insights into the tools you use like Jira, Slack, Grafana and your CI pipeline.
6. Resolve: auto-triage tickets so you can respond to customers faster.
Every mission makes your Alloy agent smarter.
Your fleet's history becomes institutional knowledge that any engineer can tap into via MCP, Slack or the platform.
Offices: Surry Hills, New South Wales, AU · San Francisco, US
Robotics Data PlatformRobotics Data InfrastructurePhysical AIRobotics ObservabilityAI Agents for RoboticsRobot Data ManagementRobotics R&DROS2Robotics Validation & TestingSensor Data Management
How many Agriculture jobs are open in Sydney, Australia right now?
There are currently 62 open agriculture positions in Sydney, Australia listed on Clera. New openings are added daily as companies post roles.
Which companies are hiring for Agriculture roles in Sydney, Australia?
Companies currently hiring include Rabobank, Spotless Group, GrainCorp, Louis Dreyfus Company, Rentokil Initial, among others. Browse the listings above to see every active employer.
Are there remote or hybrid Agriculture jobs in Sydney, Australia?
Yes — 26 of the 62 open agriculture positions offer remote or hybrid work (0 remote, 26 hybrid).
How do I apply for Agriculture jobs in Sydney, Australia?
Each listing links directly to the employer's application page. Apply early — fresh listings get the most recruiter attention in the first two weeks.