Machine Learning Engineer
Role details
Job location
Tech stack
Job description
At Vyntelligence, we're reimagining how work gets done in the deskless world. Our platform enriches expertly-curated short videos - captured before, during, and after work - with AI-powered automated workflows. We own the world's largest dataset of this kind, enabling field users and customers to make smarter, more efficient decisions about their assets and operations. Our purpose is to deliver transformative commercial and environmental outcomes, helping global clients across utilities, telecoms, retail and energy improve pricing accuracy, speed up delivery, ensure safety, and maintain compliance.
Technologies
Mobile and Web apps are used to capture, manipulate and view structured multimedia data. This data is uploaded to our public cloud systems. Then analysed using machine learning, computer vision, LLM (and Vision-Language models) and other technology. Our AI decision making agents combine with various integrations to support decisions for users, and in other systems, to drive important field workflows.
We are continually enhancing the data collection and computer vision and AI capabilities of our platform.
Our team includes multiple specialists who are developing and improving our AI capabilites. As well as a wide range of other software engineers to provide our full offering. We use github, github actions, code-pipeline and cdk with a devops/gitops approach to achieve a high release cadence through our CD pipelines. We use Django rest framework and Postgresql to provide our primary REST API interface. Our web-app is built using react. We also have android and ios native apps.
Our platform is primarily AWS. Our platform has a pluggable architecture coordinated using queues. Work is distributed to a variety of processing systems and smaller services. We use a combination of commodity analytics APIs (e.g.) and bespoke AI algorithms and models (e.g. aws transcribe, google speech, aws rekognition, claude, tensorflow, bedrock, RT-DETR) to provide advanced multi-step multi-modal speech, image and video analytics. We use aws-lambda, docker, ECS, api gateway, aws-step-functions and other tools to implement various video processing, ML and other AI applications.
As you would expect, our system also provides various collaboration, administration, management and security related features around the central video capture and analytics. We offer both shared and dedicated deployments of the software; by defining all of our infrastructure as code we are able to easily deploy dedicated copies of our entire system into dedicated VPCs for our large customers. Many of our customers have stringent security requirements around their video data, so we use a variety of modern systems to provide security and monitoring across our networks and applications.
What you'll do:
- Analyze raw data for assessing quality, cleaning and structuring for down-stream processing.
- Build and train supervised, unsupervised and re-enforcement learning algorithms for real business problems.
- Collaborate closely with the Data Science teams on the deployment and monitoring of live production models.
- NLU/NLP and computer vision-based predictions and inference for specific B2B use cases.
- Model building, validation, verification.
- Hyperparameter tuning and deployment, where necessary.
- Collaborate with the engineering team to bring analytical prototypes to production.
Requirements
Do you have experience in Terraform?, Do you have a Master's degree?, * Bachelors/Masters in Computer Science, Software Engineering, Mathematics or equivalent.
- Excellent Python and Software Engineering, 4+ years experience.
- Good knowledge of data structures, data modelling and software architecture.
- Production level experience with Keras, PyTorch, Tensorflow and Scikit-learn etc.
- Good experience with web API services and web standards (REST, SOAP etc.)
- Experience with Amazon Web Services (AWS), Cloudformation, Terraform,etc.
Benefits & conditions
We offer competitive remuneration and benefits, tax efficient employee stock
ownership plan scheme (ESOP) and private health coverage and related health
benefits are available depending on location. We provide family-friendly flexible
working time, for example to support school pickup/drop-offs, and home working.
We have developed a relaxed, collaborative, supportive, and high performance
culture. We value employee health and well-being, and offer the opportunity to
