Senior Software Engineer at Torc Robotics

Blacksburg, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, AWS, Machine Learning, Dataset Management, Automation, CI/CD, Data Science, Linux, Docker, Kubernetes, Agile, Database Architectures, Performance Testing, Capacity Planning, Documentation, Troubleshooting

Industry

Truck Transportation

Description

Duties: Responsible for leading the design and development of software tools that optimize machine learning (ML) workflows, with a particular focus on automating processes related to dataset management, model training, deployment, and monitoring. Take ownership of designing and implementing dataset management solutions that streamline the handling, preprocessing, and storage of large datasets, ensuring smooth integration into ML pipelines. Utilize Python and AWS services (e.g., EC2, S3, Lambda, SageMaker) to build scalable, robust solutions that support the efficiency of data scientists and ML engineers throughout the ML lifecycle. Collaborate with cross-functional teams to identify automation needs, define technical requirements, and create solutions that drive performance and scalability in ML operations. Lead efforts to automate model deployment processes, including designing solutions for on-device deployment, ensuring models can be reliably integrated into production environments. Drive the development of end-to-end ML pipelines with a focus on automating data ingestion, feature engineering, model evaluation, and deployment workflows. Actively participate in daily stand-ups and sprint planning, providing technical leadership by helping prioritize tasks, identify potential risks, and address blockers. Ensure consistent delivery of high-quality software in an Agile environment, leveraging CI/CD practices and automation for testing and validation. Collaborate with peers and stakeholders to maintain a focus on both the technical and business goals of the organization. Take ownership of on-call responsibilities, providing support for production systems by proactively monitoring performance and troubleshooting issues as they arise. Collaborate with engineering teams to resolve incidents, ensuring system reliability and minimizing downtime. Conduct performance testing and capacity planning to ensure that automation solutions scale efficiently and meet performance expectations. Maintain comprehensive documentation for tools, processes, and configurations to ensure consistency across the team. Continuously evaluate new technologies and methodologies to enhance the ML development infrastructure, driving ongoing improvements to the overall toolset and workflow efficiency. Requirements: Master’s Degree in Computer Engineering, Mechanical Engineering, Computer Science, Robotics, or related with 2 years’ experience as a Software Engineer or related, or alternatively a Bachelor’s Degree with 5 years’ experience as a Software Engineer or related. Experience must include: 1) Linux platform (Ubuntu, Debian, etc) and development tools (cmake, bazel, docker, ROS, git, bash); 2) tools for software development cycle (python, git, jenkins, AWS); 3) cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes) cycle (python, git, jenkins, AWS); 4) ML lifecycle management, data storage, and acquisition patterns for robotics and advanced driver assistance systems including practical experience with Python Libraries for applied data science (Pandas, Plotly, Dask); 5) data storage and database architectures, including but not limited to relational and NoSQL databases, data warehousing and clustered, distributed data stores; 6) Oncall rotation and software root cause analysis. Position is located at HQ in Blacksburg, VA but eligible to work from anywhere in the U.S. #LI-DNI

Responsibilities

Lead the design and development of software tools to optimize machine learning workflows, focusing on automating dataset management, model training, deployment, and monitoring. Collaborate with cross-functional teams to identify automation needs and drive the development of end-to-end ML pipelines.