About Stack
At Stack, we are focused on AI advancements across diverse technical domains, transforming how AI is applied in the physical realm. Our solutions are designed to navigate and understand the world with unprecedented safety, reliability, and efficiency, transforming how modern industries operate. Our decades of expertise spans diverse AI applications reflecting our commitment to exploring new frontiers and delivering excellence in AI technology.
What We're Looking For:
We are seeking passionate individuals to join our team, driven to develop and deploy advanced AI products that redefine industry standards, making processes safer, faster, and more efficient. If you thrive in fast-paced tech environments and are eager to contribute to transformative AI solutions, we invite you to join us in shaping the future of technology.
About the Team:
In Stack’s Machine Learning Platform’s ML Data team, we work on three areas: labeling, training dataset, and data mining. Labeling works with our vendor and automates the need for getting our proprietary dataset annotated and ready for training. Training dataset has two goals: build the pipelines to create the dataset used for training our models and build the infrastructure to serve datasets in high throughput. Data mining implements the infrastructure and the techniques to find interesting events on the truck and offboard while enabling the platform team and MLE teams to create hundreds of dimensions to explain our dataset slicing.
- For training, you would help us build state of the art infrastructure to support machine learning training specific read and write access patterns. This would involve OSS components such as Ray, Spark, and Iceberg.
- For mining, you would be building a high throughput inference service using LLMs and vector db. You would then help us explore in-context learning and fine-tuning for making more out of the models.
- For labeling, you would set the direction and build towards auto-labeling. You would be the owner driving labeling needs of the entire company.
What Success Looks Like:
- Experience with both ML Platforms and building ML-based applications (bonus point if you have modeling experience).
- Experience building scalable, reliable infra at a fast-paced environment.
- Ability to work across teams.
- Experience building or using ML infra built for a large number of customer teams.
- A deep understanding of design tradeoffs and ability to articulate those tradeoffs and work with others on getting alignment.
- Experience with building ML models or ML infra in the domains of autonomous vehicles, perception, and decision making (desirable but not required).
- Experience with model training, model optimization, or large data processing pipelines.
We are especially interested in individuals who check at least two of these boxes:
- Knows how to push the GPU to its limit from Python to CUDA kernel level.
- Built the inference or training loop for a large model (ideally with LLM flavor).
- Shipped ML products (NLP, computer vision, recommender systems, etc.) at scale to make business impact.
- Have data platform experience where you built infrastructure for real time querying / vector databases, batch/stream processing using Ray, Spark or similar, and Parquet-based object storage (data lake / data warehouse).
- Knows how to build low latency / high throughput batch or stream processing pipelines.
- Knows how to write (readable) high performance C++.
- Has prior AV experience. #LI-AW1
We are proud to be an equal opportunity workplace. We believe that diverse teams produce the best ideas and outcomes. We are committed to building a culture of inclusion, entrepreneurship, and innovation across gender, race, age, sexual orientation, religion, disability, and identity.
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Please Note: Pursuant to its business activities and use of technology, Stack AV complies with all applicable U.S. national security laws, regulations, and administrative requirements, which can restrict Stack AV’s ability to employ certain persons in certain positions pursuant to a range of national security-related requirements. As such, this position may be contingent upon Stack AV verifying a candidate’s residence, U.S. person status, and/or citizenship status. This position may also involve working with software and technologies subject to U.S. export control regulations. Under these regulations, it may be necessary for Stack AV to obtain a U.S. government export license prior to releasing its technologies to certain persons. If Stack AV determines that a candidate’s residence, U.S. person status, and/or citizenship status will require a license, prohibit the candidate from working in this position, or otherwise be subject to national security-related restrictions, Stack AV expressly reserves the right to either consider the candidate for a different position that is not subject to such restrictions, on whatever terms and conditions Stack AV shall establish in its sole discretion, or, in the alternative, decline to move forward with the candidate’s application.
