Principal Machine Learning Engineer, AiDash
$200-250k
Office located in Palo Alto, California
Satellite-first AI applications
Job no longer available
Satellite-first AI applications
201-500 employees
Job no longer available
$200-250k
Office located in Palo Alto, California
201-500 employees
Company mission
To create a greener, cleaner, safer planet from space.
Role
Who you are
- Minimum of 8 years of professional experience in machine learning and related domains
- Deep understanding of the machine learning ecosystem and strong experience in monitoring models and data in production environments
- Proficiency in implementing sampling strategies for diverse models and use cases
- Skilled in grading models at scale to assess and optimize performance
- Proven experience in designing and developing distributed training and inference platforms using distributed computing frameworks like PySpark, Kubeflow, and Kubernetes
- Extensive experience in Python programming, and strong familiarity with Docker for containerized application development
- Hands-on experience with tools such as MLFlow, TensorBoard, and Weights & Biases (WandB) for evaluating model performance
- In-depth knowledge of MLOps practices and cloud platforms like AWS, GCP, and Azure
- Expertise in handling large datasets for training, including experience with HDFS, Data Lakes, and both SQL and NoSQL databases
- Bachelor's / Master’s Degree in Computer Science, Mathematics & Computing, Electrical Engineering, or a related field
What the job involves
- We are looking for a Principal Machine Learning Engineer to develop and enhance our ML infrastructure and platforms, streamlining the process of building, deploying, and monitoring machine learning models. In this role, you will work with cutting-edge technologies, rapidly expanding your knowledge and skills
- You will collaborate with diverse teams across the company, and will see the direct impact of your contributions on our products and customers
- Design and build scalable platforms for training and inference of ML models
- Develop and integrate data pre-processing and post-processing workflows for seamless model deployment
- Build robust model monitoring services on top of the inference platform to ensure optimal performance
- Create platforms for large-scale model evaluation and grading
- Develop advanced tools for model experimentation to accelerate innovation
- Design and implement sampling strategies to effectively assess model performance
- Oversee the entire ML lifecycle, including design, experimentation, development, deployment, monitoring, and maintenance
- Develop reusable workflows for Data Science models and integrate them with production systems, ensuring efficiency and minimal redundancy
- Deploy production-ready code and actively participate in code reviews to maintain high-quality standards
- Refactor services to improve code quality, runtime efficiency, and resource optimization
- Build automation and active learning frameworks to streamline model retraining processes
- Lead and mentor a team of software engineers, fostering a collaborative environment and providing guidance to help them reach their full potential
Our take
The growing constellations of satellites delivering high definition earth imaging, along with advancements in AI, have spawned a flourishing business in ‘digital twins’ for global asset management. These digital data-compilation copies of on-the-ground locations can be used to help predict and mitigate the effects of climate disaster events - which cost the US billions of dollars each year.
AiDash’s various products help manage disaster, disruption and wildfires, as well as monitoring vegetation. The latter service, its original one, has a host of high-profile global clients, such as National Grid. The company's expansion into climate resilience demonstrates a keen awareness of both its tech's potential and the direction of the market - with environmental issues becoming even more of a hot topic in recent years.
The company has done well to prove its agility, which may well enable it to stay ahead of more specified rivals as they enter the scene. This includes companies like Kettle and Technosylva which operate in the predictive climate analytics space. AiDash's comparatively diverse offering should help it retain a competitive edge, while the substantial funding it has secured supports the pursuit of heightened global expansion.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 4 rounds)
Apr 2024
$58.5m
SERIES C
Nov 2022
$10m
LATE VC
Total funding: $101.5m
Company benefits
- Multiply your growth - Grow into your best self while you are helping us grow. Where are you on your career path? How can we help?
- Know your view is valued - Your concepts, your perspectives, your opinions, your creativity
- Balance work with life - You’ll need personal time for the things that matter. You’ll have it
- Get world-class rewards - Comprehensive benefits, ESOPs and other perks
Company HQ
University South, Palo Alto, CA
Leadership
Abhishek Vinod Singh
(CEO & Co-founder)Co-founded Mitrr and DbyDX Software, then moved into a SVP and COO role at Kellton Tech Solutions. Most recently spent a decade as co-founder, director, and advisor at Edureka.
Rahul Saxena
(Chief Product & Technology Officer, Co-founder)Spent time in senior Engineering roles at Qwest Software Services and RSA Security, then went on to co-found allMemoirs and Quiklo.
Nitin Das
(Chief AI Officer & Co-founder)Previously directed Applied Research at Kvantum, then acted as Lead AI Scientist for Target. Additionally co-founded Uvaca Private Ltd.
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