Steve Thomas
Verified Expert in Engineering
Software Developer
Steve is a software engineer passionate about machine learning and automating data-intensive workflows. His goal is to free scientists and BI analysts from creating and maintaining complex cloud environments so they can focus on what they do best. He has experience collaborating with the interdisciplinary perception teams at some of the top autonomous vehicle companies and building tools to automate the model training lifecycle.
Portfolio
Experience
Availability
Preferred Environment
Amazon Web Services (AWS), Linux, Kubernetes, Docker, IntelliJ IDEA, PyCharm
The most amazing...
...thing I've developed was my company's entire machine learning experiment tracking product offering so users could collaborate and visually compare their runs.
Work Experience
Software Engineer/Resident Deep Learning Expert
Engine ML
- Designed and programmed our local, freemium offering that allowed users to run deep learning experiments on their own hardware, persist all relevant logs and metrics, and compare the results in the engine dashboard alongside their cloud jobs.
- Led research showing how layer-wise optimizers (e.g. LAMB) can train object detectors with large batch sizes in a fraction of the time without performance degradation. Results can be found on our company blog at https://bit.ly/35gfM0P.
- Designed and programmed an alerting service that notified users when their experiments entered a terminal state or when their experiments potentially entered a race condition by analyzing the experiment's log output and GPU utilization.
- Designed and programmed a feature to pre-fetch training data from S3 buckets, storing it in an in-memory read-through cache using Alluxio and Alluxio’s FUSE-based POSIX API, resulting in up to a 5x speedup when reading a remote file.
- Built a cat detector that was trained live in five minutes on 64 GPUs at VentureBeat Transform 2019 using a TensorFlow implementation of RetinaNet. The demonstration by our CEO can be found at https://bit.ly/2YdMbnr.
Independent Machine Learning Researcher
Self-employed
- Achieved the status of “Top Contender” in The Lyft Perception Challenge 2018, a semantic segmentation competition, using a tweaked version of Google’s DeepLabV3 with ResNet-152 as the backbone.
- Designed and integrated perception, behavior planning, trajectory generation, and controller modules so Udacity’s driverless car could safely navigate a road with traffic lights (https://github.com/sathomas2/CarND-Capstone-Solution).
- Taught myself the major developments in deep learning and the mathematical theory behind them by reading and replicating papers.
Experience
Training Mask-RCNN 10x Faster with LAMB
Train a Cat Detector Live on 64 GPUs in Less Than Five Minutes
https://www.youtube.com/watch?v=GKVbPFpEBHk&feature=youtu.be&t=6724Skills
Languages
Python, Kotlin, C++
Libraries/APIs
TensorFlow, PyTorch
Platforms
Docker, Kubernetes, Amazon Web Services (AWS), Linux
Other
Leadership, Communication, Teamwork, Deep Learning, Algorithms, Machine Learning, Object Detection, Computer Vision, Analytical Thinking, Robot Operating System (ROS), Localization, PID Controllers, Reinforcement Learning
Frameworks
Django
Tools
PyCharm, IntelliJ IDEA, Gradle
Storage
Elasticsearch, InfluxDB
Education
Master's Degree in Philosophy
New York University - New York, NY
Bachelor's Degree in English and Economics
Bowdoin College - Brunswick, ME
Certifications
Self-Driving Car Engineer Nanodegree
Udacity
Graph Search, Shortest Paths, and Data Structures
Coursera
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
Coursera
Deep Learning Foundation Nanodegree
Udacity
Machine Learning
Coursera
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
Share your needs
Choose your talent
Start your risk-free talent trial
Top talent is in high demand.
Start hiring