At Halter, weâre on a mission to enable farmers and graziers to run the most productive and sustainable operations. Our customers are using Halter to break free from the time-intensive constraints of conventional practices. Imagine watching 500 cattle stand up and walk calmly towards their next break? No quad bikes, no dogs, no fences. Just a group of cattle walking at their own pace. People say it looks like magic. Our customers are revolutionizing grazing with Halter. It's changing lives and transforming an industry. People join Halter to do meaningful work. By joining us youâll be solving challenging problems within a talented team and a culture built for high performance. Our team out-think, out-work and out-care. Weâre committed to delivering real change in the world - this isnât easy, and in truth, we love that itâs hard.
Weâre backed to deliver on a mission that matters by Tier 1 investors including Founders Fund, Bessemer Venture Partners, BOND, DCVC, Blackbird, Promus Ventures, Rocket Labâs Peter Beck and Icehouse ventures.
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Data is in our DNA at Halter, and represents one of our highest leverage assets for delivering value to farmers and ranchers. The performance of Halterâs predictive models are hugely dependent on the quality ground truth data backing them. As such, weâre building a dedicated machine learning Data Operations function to own the full lifecycle of ground truth datasets: from collection of measurements with wide geographic diversity, through to annotation, and quality controlâso that our ML teams can innovate with urgency, and ship reliable, highâimpact capabilities to farmers.
You will design, build, and run repeatable, scalable ground-truth dataset building pipelines. The work blends operational leadership, data quality governance, and close partnership with ML engineering.
Own end-to-end ground truth programmes: define collection methodology, sampling strategy, annotation guidelines, review flows, and delivery milestones from concept to âmodel-readyâ datasets.
Partner with ML engineers/researchers to translate model needs into label schemas, taxonomies, and measurable acceptance criteria (what âgoodâ looks like).
Facilitate and execute plans to annotate data, using guidelines from client machine learning teams.
Build a quality control system to enable effective distribution of work, while maintaining high label quality.
Establish scalable QA processes that catch label noise early and reduce rework and downstream model risk.
Define dataset standards: metadata, versioning, provenance/lineage, update cadence, and âdataset contractsâ for ML consumption.
Build lightweight but robust tooling and documentation so datasets are discoverable, reproducible, and safe to use.
Stand up flexible labour capability for collection/annotationâincluding vendor selection, onboarding/training, QA enforcement, throughput planning, and performance management.
In the first several months, this role is about building the machineânot only doing the work.
Within 90 days: a clearly defined ground-truth lifecycle (intake â collect â label â QA â version/release), initial label schema/taxonomy for priority ML problems, and baseline quality metrics. Comparable roles explicitly focus on defining standards and annotation frameworks early.
Within 6â12 months: predictable throughput and lead time, proven QA effectiveness (lower rework / higher agreement), and expanded regional capacity through repeatable vendor/contractor workflowsâwhile maintaining a consistent quality bar.
Demonstrated ownership of a process comparable to a data/annotation operation end-to-end (collection and/or labelling), including quality systems and delivery against schedules.
Comfort translating ambiguous ML needs into precise operational specs: label definitions, edge-case handling, escalation rules, and measurable acceptance criteria.
Capability to move with urgency: Of the Halter Operating principles, moving with urgency is particularly relevant. Data collection is one of the most laborious phases of model development. The faster it moves, the faster we can deliver value to farmers and ranchers.
Strong service mindset: you are passionate about making the cross-functional system work, and passionate about the success of your client teams (ML engineering teams).
Experience managing thirdâparty workforces (BPO, crowd, field contractors) and enforcing quality at scaleâeither as a client-side owner or within an annotation/data services provider.
Familiarity with annotation tooling and workflows (e.g., CVAT/Label Studio/Labelbox-style concepts), and dataset versioning/metadata practices.
Exposure to real-world sensor data, computer vision, time series, or edge deployments (relevant to âground truth measurement collectionâ and field variability).
Experience in highly regulated or safety-critical environments (healthcare, autonomy, defence) where data quality and provenance are non-negotiable
Thereâs a reason you visit your friends in person, live with your family and donât do dinners over Zoom. Humans are wired for connection. We believe a world-class, in-person office culture is the best way for high-performing teams.
Being office first is a core pillar of our culture. We believe in-person connections are key to driving your own growth, learning, impact, and building genuine long-lasting relationships. Strong relationships make it easier to disagree, give feedback, and do meaningful and aligned work. We donât like having heaps of rules or policies, but this means having strong, trusted relationships is critical.
Weâre office first, not office only. This means working from the office everyday is our default setting, but we flex when we need to. We have a high-trust culture, so everyone is trusted to do whatâs best for Halter.
Our office vibe is something special, itâs hard to describe until youâre here, but people at Halter who have come from fully remote or hybrid companies say they could never go back - the high energy and spectacular people they are now surrounded by everyday makes work so enjoyable. Your growth, your learning and your impact is truly unlimited here, and a big part of that comes from being together solving problems, innovating, building context, and constantly learning from each other.
Halter is committed to promoting a diverse and inclusive workplace â a place where we can each be ours
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