ML Engineer
About Dockware
Dockware is relentlessly driving toward the error free dock.
Every day across the world, millions of freight handlers scan, count, inspect, measure, photograph, label, read, and transcribe shipment data tens of millions of times, and errors are the rule, not the exception. Every error on the dock ripples across the rest of the value chain, leading to inventory management discrepancies, misroutes, under/over pricing, disputes, sub-optimal asset utilization, and many flavors of claims and chargebacks. As logistics underpins roughly 10% of global GDP, even marginal improvements at the dock translate into massive gains for businesses, workers, and consumers everywhere.
Using computer vision, edge hardware, and real-time ML systems, Dockware captures dimensions, condition, and identifiers for every shipment - in seconds. That data becomes a verifiable digital twin: a continuously updated, shareable record that moves with the shipment from origin to destination.
Our platform integrates directly into existing workflows, eliminating manual data entry, barcode dependency, and costly delays - while turning messy, real-world freight into clean, actionable data. The result is a system that reduces disputes, improves operational accuracy, and unlocks better decisions across the supply chain. For the full company vision, read here.
Dockware has deep Midwestern roots. Jerks, credit hogs, and elitists need not apply. We come to work and give everything because we know our teammates are doing the same. Similarly, the logistics professionals we serve make modern life possible – full stop. They deserve tech that works and real humans who answer the phone.
Fill out the job application, if interested.
About the Role
We're looking for an ML Engineer to build, deploy, and maintain the infrastructure that powers our core computer vision products. You will primarily focus on creating robust, scalable machine learning pipelines which include data versioning, model training/versioning, evaluation, and deployment, while supporting Vision (our autonomous sensor suite) and Scan (our mobile application).
- Vision App: Infrastructure supporting autonomous sensor suite's computer vision models.
- Scan App: Infrastructure supporting the mobile application's freight dimensioning and tracking.
- ML Pipelines: Design and own scalable pipelines for data versioning, labeling, model training, evaluation, and continuous deployment of custom computer vision models.
- Backend & infra: APIs, data pipelines, and deployment systems supporting all products, leveraging AWS and serverless compute.
The role requires both building and testing new features as well as maintaining production systems. You'll ship infrastructure that directly supports computer vision models running on warehouse floors and in the hands of logistics operators daily.
What We're Looking For
Required:
- High ownership mentality and comfort with significant autonomy—you see problems through to resolution, whether that means debugging infrastructure or redesigning entire ML workflows.
- Expertise in building and scaling custom machine learning models in production environments.
- Strong experience with MLOps principles, including data and model versioning, labeling, training, and evaluation pipelines.
- Familiarity with at least some of our core stack and interest to learn the rest:
- MLOps Tools: MLflow, DVC, Ultralytics
- Infrastructure: Docker, AWS (Lambda, SQS, ECS), Serverless Compute, CI/CD, testing suites, Github Actions, K8s/K3s, Vertex AI, Terraform
- Backend: FastAPI, Pydantic, Postgres, SQLite
- Genuine interest in solving hard problems in logistics and computer vision.
Bonus Points for:
- Experience specifically deploying computer vision models (e.g., using technologies like ONNX or TensorRT).
- Track record of shipping production software systems in a high-autonomy environment.
- Experience with large-scale data pipeline construction and optimization.
- Experience in the shipping or logistics industry.
What Makes You Successful Here
You don't get stuck in the weeds. You commit and follow through. You're comfortable with ambiguity and can make progress without complete specifications. You ask "why" before diving into "how," and you're not afraid to challenge assumptions when something doesn't make sense.
This is a high-ownership environment where your work directly impacts customers and revenue. You'll have autonomy, but you'll also be expected to take initiative and drive projects to completion.
Location
Striking distance of Tulsa, OK preferred, but we're open to exceptional candidates regardless of location. This role may require periodic travel to customer sites and our Tulsa headquarters.
Dockware is building the primary data capture layer for global logistics. If you're ready to tackle computer vision challenges at warehouse scale while keeping world-class engineers aligned and shipping, we want to hear from you.