About the Company:
At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.
Meet The Team:
Torc’s Data Engineering team is a highly collaborative group focused on building secure, scalable systems that let customers work with data easily and confidently. We operate as a distributed, remote-first team, ensuring everyone has equal access to information and collaboration. Our work includes frequent touchpoints—daily standups, planning sessions, and ad hoc discussions over Zoom or Slack. Because we own our services end-to-end, each engineer participates in a business hour on call rotation. We prioritize healthy work/life balance and sustainable development over heroic efforts.
What You’ll Do:
We are looking for a Software Engineer who is eager to learn and grow while helping build and support Linux- and cloud-based data systems. In this role, you’ll work closely with experienced engineers to contribute to AWS-based data ingestion, ETL, and storage solutions that enable analytics, simulation, and ML training across the company.
- Create robust and resilient pipelines to process massive daily volumes of data created by vehicle fleets and simulation environments
- Build and support scalable pipelines as part of Torc’s Data Factory to deliver data for ML training at scale
- Scale Torc’s data lake through a distributed storage system, data crawling and discovery
- Promote and protect the integrity of data through validation, versioning, data provenance, and governance
- Support the expansion of Torc’s data lake through acquisition of additional data sets from internal and external sources
- Assist in the development and delivery of cloud-based solutions
- Collaborate with teams specializing in perception, planning, control, mapping and vehicle testing to develop solutions that support product delivery
- Support the implementation of emerging cloud-based capabilities that can extend our technology stack and improve our ability to build, deploy and test safety-critical software for self-driving vehicles
- Participate in the team’s on-call rotation to support our deployed systems during business hours
Here’s a list of some of the technologies we use to make all the above happen:
- Managed services powered by AWS (Lambda, SFN, Batch, EventBridge, Athena, Glue)
- Linux / Bash
- Docker
- Terraform
- Python
- React/Javascript
- On-Call Tooling (Datadog, AWS Cloudwatch)
What You’ll Need to Succeed:
- Bachelor’s degree in Computer Science, Software Engineering, or related field with 6+ years of professional experience
- Master’s degree in Computer Science, Software Engineering, or related field with 3+ years of professional experience
- Strong proficiency writing code using Python
- Practical experience with Docker and containerization
- A strong commitment to test-driven development patterns, continuous integration and delivery, and infrastructure as code
- Experience with Linux and general bash scripting
- Experience deploying, troubleshooting, monitoring and maintaining Linux systems
Bonus Points!
- Strong organizational, time management, and communication skills working with a team orientation and collaborative style
- Experience developing cloud-based serverless solutions
- Experience with pandas, numpy and other Python-based data analysis libraries and tooling
- Deep knowledge of AWS serverless architectures (Lambda, Batch, ECS Fargate, Glue, Athena)
- Experience with data storage and acquisition patterns for robotics and advanced driver assistance systems
- Knowledge of different database architectures, including but not limited to relational and NoSQL databases, vector stores, data warehousing and clustered, distributed data stores
- Experience with the Databricks platform, particularly for serving data, visualizations and jobs
- Experience with scaling data for ML and AI workloads using Ray