Description

SAIC is seeking an individual for a Senior Principal Machine Learning Engineer role to join the AI Mission Accelerator’s Data Science team. This opportunity allows for full-time telework from anywhere within the U.S.

SAIC is a premier technology integrator solving our nation’s most complex modernization and readiness challenges. Our robust portfolio of offerings across the defense, space, civilian, and intelligence markets includes high-end solutions in engineering, IT, and mission solutions. Using our expertise and understanding of existing and emerging technologies, we integrate the best components from our own portfolio and our partner ecosystem to deliver innovative, effective, and efficient solutions. 

The ideal candidate will have strong experience directly building, deploying and operating machine learning systems. They must have a proven ability to drive business results using artificial intelligence-based solutions, in support of various customer missions within the Federal Government and other Public Sector organizations. You would support a variety of programs including customer deliveries, IRAD projects, and bid and proposal work.

The Senior Principal Machine Learning Engineer role will work in a collaborative, team-focused environment. This position will help progress, establish, incorporate, and advance current and promising concepts to inform tactical decision-making timelines. Your role is to provide needed technical knowledge in the areas of Computer Vision, Natural Language Processing, AI/ML, and Data Fusion & Analytics to develop algorithms, support modeling and simulation efforts, and provide data analytics skills to deliver production-hardened solutions.

Responsibilities:

• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions that leverage machine learning.
• Design, build, and maintain complex machine learning systems, models, and pipelines for production.
• Build reusable libraries and frameworks for serving machine learning models as performant APIs.
• Evaluate new machine learning research and technologies and determine feasibility for adoption.
• Establish MLOps best practices for machine learning development, testing, deployment, and maintenance.
• Improve existing machine learning systems by enhancing models, algorithms, and pipelines.
• Identify opportunities where machine learning can add value to business goals and operations.
• Communicate complex machine learning concepts clearly and simply to non-technical audiences.
• Contribute to documentation, knowledge sharing, and training to ensure the transfer of technical knowledge within the team.

Qualifications

Required Qualifications:

• Must be a US Citizen
• Bachelor's degree in Mathematics, Physics, Information Technology field, Operations Research or related discipline with nine years of experience. In lieu of a degree, four additional years of experience will be considered.
• Proficiency in programming languages such as Python and with machine learning libraries such as PyTorch and scikit-learn.
• Experience developing and consuming REST APIs and microservices.
• Knowledge of continuous integration/delivery patterns and tools (e.g., Jenkins, Azure DevOps, GitHub Actions, etc.).
• Hands-on experience with MLOps tools (e.g., MLflow, Neptune, Azure ML, etc.). 
• Excellent communication and collaboration skills.

Desired Qualifications:

• Experience with cloud platforms such as AWS, GCP, or Azure.
• Proficiency with containerization technologies like Docker and Kubernetes.
• Ability to optimize machine learning models and pipelines for speed and scalability.
• Experience applying computer vision techniques such as object detection, instance or semantic segmentation, etc.
• Familiarity with computer vision libraries and frameworks such as OpenCV, TorchVision, Albumentations, Detectron2, etc.
• Experience applying natural language processing techniques such as named entity recognition, summarization, etc.
• Familiarity with transformer models and libraries.
• Familiarity with multimodal models.