What are the responsibilities and job description for the Software Engineer, ML Infrastructure position at Serve Robotics?
Job Description
Job Description
At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles while doing commercial deliveries.
The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.
Who We Are
We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.
What you'll be doing
Serve’s Machine Learning (ML) platform is an important core part of our autonomy. It empowers us to train and test all kinds of ML models for various real-world tasks. We also use it to mine useful data in terabytes of sensor recordings that we capture every day.
We are looking for an engineer who will join our Machine Learning Infrastructure (ML Infra) team on a mission to build and improve this platform. We use Apache Beam (Dataflow) for our pipelines, Bazel as our build system, BigQuery via dbt, MongoDB and GCS for storage, Kubernetes for service deployment, and Airflow for our workflow management.
Responsibilities
Develop and maintain ML infrastructure, such as sensor data ETL pipelines, hard example data mining, continuous training pipelines, annotation platform, etc.
Develop MLOps system for managing lifecycle of ML cloud training and inference as a service pipelines. Continuously improve ML model development, management and deployment processes.
Work together with ML engineers, design metrics for ML tasks to mine sensor data of interest.
Design and implement algorithms, such as collaborative filtering, active learning, etc., to rank / score annotation candidates.
Work with annotation provider on setting up the annotation process, quality control and feedback loops.
Make sensor data and its derivatives widely discoverable and accessible for Robotics Engineers across the entire company.
Qualifications
BS or MS in computer science with focus in data engineering and large scale ML systems
2 years of industry experience building, running and improving large-volume ML training and validation pipelines.
Experience with building native cloud applications
Experience with large scale data processing pipelines in production.
Proficient in at least one of the following languages : C , Python, or Go.
Hands-on experience and good knowledge of Computer Vision and Deep Learning.
Strong tendency to automate own and others’ workflows.
What makes you standout
Experience with data discovery and visualization tools like Voxel51, Facets
Experience with database systems like BigQuery, MongoDB
Experience with Nvidia Jetson platform, e.g. CUDA, TensorRT, etc.
Experience with Big Data products such as Apache Beam / Spark / Hadoop, GCP BigQuery, AWS Redshift.
Compensation Range : $119K - $160K
Salary : $119,000 - $160,000
