Machine Learning Engineer leading independent applied-research projects for defense-focused missions. Engaging with novel AI and ML technology to deliver mission-scale capabilities.
Responsibilities
Design, implement, and evaluate state-of-the-art ML models (computer-vision, NLP, planning, etc.) using frameworks such as TensorFlow, PyTorch, Torch, or Caffe.
Build and maintain robust data pipelines, ETL processes, and backend services in Python, C/C++, and Java.
Lead rapid-prototyping efforts, translate research results into operational prototypes, and test for performance, robustness, and security.
Define and refine DevSecOps practices for ML (model registries, containerized deployment, continuous integration/continuous delivery, security scanning).
Mentor junior team members, collaborate with researchers, government customers, and other engineers, and contribute to technical strategy for the lab.
Requirements
B.S. in Computer Science, Electrical Engineering, Statistics, or related field with ≥10 years of experience; OR M.S. with ≥8 years; OR Ph.D. with ≥5 years of relevant experience.
Ability to obtain and maintain an active Department of War security clearance.
Strong experience in one or more programming languages such as Python, C/C++, and Java; comfortable developing production-grade code and APIs.
Solid understanding of ML theory, statistical learning, and common algorithms.
Hands-on experience with TensorFlow, PyTorch, Torch, Caffe, or similar deep-learning libraries.
Familiarity with CI/CD pipelines, container orchestration (Docker/Kubernetes), model versioning, and security-focused tooling.
Proven track record of independent applied-research projects that resulted in demonstrable prototypes or operational capabilities.
Publications or open-source contributions in AI and ML, especially in adversarial or robust ML.
Experience working on defense or other high-impact government programs.
Ability to quickly learn emerging AI and ML technologies and translate them into mission-relevant solutions.
Deep technical knowledge of modern ML methods and ability to extend them to novel domains.
Excellent written and verbal communication skills; capable of presenting complex ideas to technical and non-technical audiences.
Strong collaborative mindset; experience working in interdisciplinary teams and mentoring peers.
High degree of scientific curiosity and a proactive, self-directed work style.
Benefits
comprehensive medical, prescription, dental, and vision insurance
generous retirement savings program with employer contributions
tuition benefits
ample paid time off and observed holidays
life and accidental death and disability insurance
free Pittsburgh Regional Transit bus pass
access to our Family Concierge Team to help navigate childcare needs
Principal Software Lead developing and optimizing AI models at Standard Bots, a robotics startup. Collaborating closely with the engineering team on large - scale AI models and experiments.
Applied Machine Learning Engineer designing and optimizing circuits in a multi - functional team at NVIDIA. Working on various silicon - related projects and utilizing machine learning techniques for circuit design and analysis.
Machine Learning Engineer intern working with Toyota on estimation algorithms for vehicle control. Collaborating with experts in AI and robotics for human - centered automated driving solutions.
Software Engineer on perception mapping team at Zoox, designing and integrating ML models for autonomous vehicle mapping. Collaborating on online mapping initiative to enhance self - driving capabilities.
Staff AI/ML Engineer designing and implementing cloud technologies for self - driving vehicles at GM. Collaborating on large - scale initiatives and mentoring engineers for project success.
Senior AI/ML Engineer focusing on designing scalable ML infrastructure solutions at GM. Collaborating across teams to solve complex problems and mentoring junior engineers.
AI/ML Engineer developing intelligent automation solutions using generative AI and machine learning technologies. Ensuring operational efficiency and continuous refinement of integrated AI/ML solutions with a focus on modern engineering.
Manager II leading a team of engineers developing machine learning systems for content recommendations. Driving technical direction and stakeholder communication to enhance user engagement at Pinterest.
Principal Machine Learning Engineer leading technical direction for growth initiatives at Pinterest. Collaborating with product teams and advocating for engineers to drive user engagement and platform innovation.