Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
Experience with computer vision, generative AI models, large language models, or agentic systems
Familiarity with ML evaluation frameworks and agentic model design
Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:
$240,450 - $300,300 USD
Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of Washington DC, Texas, Colorado, Hawaii is:
$216,300 - $269,850 USD
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. T
Benefits
Health insuranceDental insuranceVision insurancePaid time offEquity / stock options
Additional Information
The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and unique access to massive datasets to deliver improvements to our customers.
Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge . Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications.
You will:
Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
Work with massive datasets to develop both generic models as well as fine tune models for specific products
Build scalable machine learning infrastructure to automate and optimize our ML services
Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
Comfortable with light travel (approximately 10%) for customer interaction and team needs
This role will require an active security clearance or the ability to obtain a security clearance
Ideally You'd Have:
Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
Solid background in algorithms, data structures, and object-oriented programming
Strong programing skills in Python, experience in Tensorflow or PyTorch
Scale Ai · San Francisco, CA