We wrap our arms around businesses and help them scale.
We work with enterprises and fast-growing companies to implement AI strategy, workflow automation, and build custom software.
Not as a vendor, but as a genuine partner with skin in the game.We move at a pace most businesses have never experienced, and we hold ourselves to a standard most agencies can't match.
We don't pitch solutions.
We sit with our clients, understand where they're stuck, and build the systems that get them unstuck.
Quickly and properly.The work is the kind engineers actually want: agent-first platforms running national field operations at around 90% automation, electric vehicle operating systems for everything from cars to trucks and buses, AI making real operational decisions in regulated industries, and rescues of platforms other teams couldn't deliver.
And for the right businesses we go further.
We unlock them: we build new products we come up with together, and we bring in capital through equity or debt to fund the growth those initiatives create.Our founders have built energy flex markets, led transactions of billion-dollar businesses, and built software agencies that consistently deliver engineers in the top 3% globally.
That pedigree shapes how we think, how we work, and what we expect from the people who join us.About 10x LabsWe wrap our arms around businesses and help them scale.
We work with enterprises and fast-growing companies to implement AI strategy, workflow automation, and build custom software.
Not as a vendor, but as a genuine partner with skin in the game.We move at a pace most businesses have never experienced, and we hold ourselves to a standard most agencies can't match.
We don't pitch solutions.
We sit with our clients, understand where they're stuck, and build the systems that get them unstuck.
Quickly and properly.The work is the kind engineers actually want: agent-first platforms running national field operations at around 90% automation, electric vehicle operating systems for everything from cars to trucks and buses, AI making real operational decisions in regulated industries,



and rescues of platforms other teams couldn't deliver.
And for the right businesses we go further.
We unlock them: we build new products we come up with together, and we bring in capital through equity or debt to fund the growth those initiatives create.Our founders have built energy flex markets, led transactions of billion-dollar businesses, and built software agencies that consistently deliver engineers in the top 3% globally.
That pedigree shapes how we think, how we work, and what we expect from the people who join us.The roleOur models don't live in notebooks.
They forecast energy production and demand, score decisions inside agentic workflows, detect faults in fleets of field devices, and drive dispatch choices with money and grid stability attached.
When the model is wrong, someone notices by lunchtime.You'll own the full loop: the data pipelines that feed the models, the models themselves (classical ML earns its keep here: random forests and gradient boosting alongside time-series methods), and the deployment, monitoring and retraining that keep them honest in production.
You'll work close to the operational systems your outputs drive, not three teams away from them.What you will doBuild and run feature and data pipelines for training and inference on high-volume, time-series-heavy dataDevelop, evaluate and tune models: forecasting, optimisation scoring, anomaly and fault detectionDeploy models into production systems with proper versioning,



monitoring and drift detectionWork with our AI engineers to embed model-driven scoring inside agentic workflowsValidate model behaviour against historical data and simulation before it touches operationsExplain model decisions to clients and non-technical stakeholders in plain languageWhat you bring — RequiredStrong Python and SQL across the full ML lifecycle: data prep, training, evaluation, deploymentProduction ML experience: your models have run live, been monitored, drifted and been fixedDepth in classical ML (gradient boosting, random forests, regression) and knowing when it beats deep learningSerious time-series experience: forecasting, seasonality, late and messy dataEngineering fundamentals: your pipelines are tested, versioned and reproducibleStrongly preferredEnergy forecasting or optimisation experience (generation, demand, price)Streaming/event-driven data infrastructure (Kafka or similar)MLOps tooling: experiment tracking, model registries, automated retrainingFlex market and energy systems experience is always a big bonusNice to haveOptimisation methods (linear/mixed-integer programming) for dispatch-style problemsLLM evaluation experience alongside classical MLWhy this roleYour models will move real assets and real money in near real time, and you'll see the feedback loop close daily.
Small senior team, no research theatre, and the rare setup where the person who builds the model also owns how it behaves in the wild.What you getA competitive package that reflects the calibre we hire atAccess to every AI tool on the market (as long as it has passed security) with team support and budget to get you set up and moving fastHard, interesting problems: real AI in production, real scale, real consequences.
Not demos and decksDirect access to some of the most experienced operators in the countryA team that will stretch you, back you, and make you betterThe interview processOur process is straightforward and moves fast: a short video interview (we know engineers hate this — the only people who don't hate it are in sales — but it's an key and necessary step, and if you can't push outside your comfort zone, you're not going to fit in here), then a technical deep-dive on real problems, not puzzles, then a final conversation with the founders.
We move quickly for the right person.
#J-*****-Ljbffr

📌 Data / Ml Engineer (New South Wales)
🏢 10x Labs
📍 New South Wales