Role overview
SAIC is seeking a machine learning engineer with hands-on experience deploying machine learning and artificial intelligence models. Experience with on-premises deployment of large language models (LLMs) is desired. The candidate will join the dynamic team at the Identity and Data Sciences Laboratory (IDSL). The IDSL is tasked with evaluating how new AI technologies can be best integrated into operational use-cases across the US government. We are investigating how AI systems can improve process efficiency and effectiveness as well as how to optimally team AI systems with human operators. Our engineers enjoy a great work-life balance and work in an environment that promotes learning and exploration.
The IDSL is based in Upper Marlboro, MD. Up to 20% travel is expected within the continental US. This is a hybrid position with 3 days per week expected in the office.
What you'll work on
- Deploy, configure, and maintain machine learning models and large language models (LLMs) on GPU-enabled on-premises servers and cloud infrastructure, ensuring high availability and reliability for all team stakeholders.
- Design and implement secure, efficient data pipelines for AI systems within air-gapped and networked environments.
- Develop and integrate agentic AI pipelines leveraging open-source LLMs and on-premises GPU hardware.
- Contribute to in-person data collections at the Maryland Test Facility.
- Stay current with the rapidly evolving AI hardware and software landscape, identifying and recommending improvements to tooling, infrastructure, and deployment practices.
- Collaborate frequently with software, networking, data science, and cloud engineering teams to align AI infrastructure with existing laboratory infrastructure.
- Work closely with the lead AI scientist to carry out these responsibilities.
What we're looking for
Required:
- BS in computer science, machine learning, computer vision, biometrics or a related field.
- Strong programming skills in languages such as Python and version control systems such as Git.
- Hands-on experience deploying and configuring LLMs, including an understanding of prompt engineering techniques.
- Experience specifying and working with GPU hardware to meet the performance demands of AI workloads.
- Experience with AI frameworks like TensorFlow or PyTorch.
- Experience deploying and configuring LLMs and an understanding of prompt engineering.
- Excellent analytical, communication, and problem-solving skills with the ability to work effectively across multidisciplinary teams.
Desired:
- Familiarity with biometric and identity systems, including face, iris, or fingerprint recognition technologies.
- Familiarity with RAG, Vector Stores, and Agentic AI.
- Familiarity with cloud platforms such as AWS, including managed AI and compute services (e.g., Amazon Bedrock).
- Exposure to secure or air-gapped deployment environments and associated data handling requirements.
- Awareness of current and emerging AI hardware options and a demonstrated habit of tracking developments in the field.
- Experience operating Agentic AI coding tools (e.g., Claude Code) in a safe, structured, and productive manner.
Target salary range: $80,001 - $120,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.