Senior Machine Learning/MLOps Engineer
Vor 4 Tagen
This is a Senior Machine Learning/MLOps Engineer role with one of the leading companies in AU right now 🔥 -- Anduril Industries -- with an amazing team. They are continuing to grow rapidly. This is the chance to join right as the 🚀 takes off.
More About the Role at Anduril Industries 🧐🧐
At Anduril Australia we are developing un-crewed maritime and air domain systems that leverage unsupervised autonomy for the delivery of long endurance, multi-mission capability to our customers. These projects, including but not limited to the Extra Large Autonomous Undersea Vehicle (XL-AUV) Program, require a Senior Machine Learning Engineer with deep experience developing, testing, validating and deploying machine-learning based sensor processing and the technical toolchains they rely on. The Senior Machine Learning Engineer strives to leverage their experience to rapidly architect, design, deliver, support, and evolve next generation autonomous vehicles through the entire product life-cycle. ### WHAT YOU'LL DO - Lead the design, development, integration and support of machine learning models for perception and data pipelines for a variety of autonomous systems across multiple sensor modalities - Develop software to manage and automate the machine learning lifecycle, including data management, pipelining, labeling and training - Manage model selection, training, validation, evaluation, deployment and regression testing - Manage training data sourcing and integration, and quality - Develop tools and processes for machine learning in a classified environment - Collaborate with cross-functional teams, including software engineers, mechanical engineers, and systems engineers, to ensure effective system integration and testing ### REQUIRED QUALIFICATIONS - Bachelor’s degree in Robotics, Mechatronics, Computer Science, Engineering, a relevant field, or equivalent experience - Experience in a senior role with the delivery of a production grade software system with a mature machine learning component - Experience with the design, implementation and maintenance of ML Operations, including data acquisition, labeling, data curation, pipeline management, continuous integration, model versioning, and monitoring - Ability to collaborate with data scientists and stakeholders to define and implement robust validation and verification strategies - Experience with the delivery of computer vision capabilities, from the data side as well as the model side - Capacity to work holistically on machine learning enabled capabilities up and down the software stack and through lifecycle through design, implementation, operation and sustainment - Capacity to learn and grow individually, while mentoring junior team members effectively, building team cohesion and capacity - Ability to obtain and maintain an Australian Government Security Clearance ### PREFERRED QUALIFICATIONS - Experience with the delivery of capability with a public sector customer - Experience with workflow management tools such as flyte, airflow, and kubeflow - Experience with deep learning frameworks such as PyTorch - Experience with edge ML applications - Experience with inference engines such as TensorRT - Experience writing C++, Go, Python or Rust - Proficiency in data management and traceability techniques to maintain high-quality datasets and enable reproducibility of experiments and results - Desire to grow in to a Tech-Lead-Manager role, with responsibility for line management of engineers, in addition to delivery
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