Postdoctoral In Bayesian Statistics Jobs in Denver, CO

At Anduril, our Software Engineers specialize in solving complex, real-world problems through cutting-edge algorithms and intelligent software integrations. Operating in small, innovative teams, we push the boundaries of what's possible to deliver advanced technologies with mission-critical applications. Our commitment doesn't end with academic research or proof-of-concept experiments; we measure our success by the real-world impact of our deployed solutions. Inspired by Arthur C. Clarke's vision - "Any sufficiently advanced technology is indistinguishable from magic" - we aim to achieve groundbreaking results in target tracking, state estimation, and situational awareness. We are currently seeking highly talented technologists to join our mission and redefine the future of defense technology.

• Define and influence the direction of a small team, leveraging your subject-matter expertise in target tracking and state estimation.
• Prototype and deploy state-of-the-art algorithms for tracking, multi-sensor data fusion, and state estimation in agile, iterative development environments.
• Develop high-performance software for real-time systems, ranging from tactical implementations to simulation environments and decision support tools.
• Design and implement robust filters, estimators, and probabilistic reasoning systems that enable actionable insights from noisy, ambiguous, or incomplete sensor data.
• Analyze system performance using high-fidelity simulations, innovative modeling tools, and rigorous statistical techniques to validate the benefits of our technology.
• Drive customer success by customizing algorithms and software for mission-critical use cases, including real-time tracking and sensor fusion.
• Integrate tracking and estimation technologies into the broader software development lifecycle, from requirements definition through testing and optimization.
• Translate technical progress into clear, actionable insights for diverse stakeholders, including colleagues and end-users.

• Proficiency in algorithm design, software development, and statistical modeling with programming expertise in C/C++, Python, and Matlab.
• Strong knowledge of target tracking techniques, such as Kalman filters, particle filters, and multi-target tracking algorithms (e.g., JPDA, MHT, or PHD filters).
• Experience in state estimation, including Bayesian filtering, sensor fusion, and recursive estimation techniques.
• Solid understanding of applied mathematics, including linear algebra, optimization, probability, and stochastic processes.
• Knowledge of signal processing techniques for interpreting diverse sensor data (e.g., radar, lidar, EO/IR).
• Familiarity with big data pipelines, NoSQL databases, and the efficient handling of large-scale sensor data.
• Background in machine learning as applied to target tracking and recognition, including clustering, classification, and anomaly detection techniques.
• Ability to engineer robust systems for estimation theory, adaptive filtering, controls, and complex signal environments.
• Demonstrated ability to work across development lifecycles, from prototyping to optimizing production systems.
• Eligible to obtain and maintain an active U.S. Top Secret security clearance.

We request transcripts as part of the early application process to understand your academic background and how your coursework supports the skills deemed critical for the role. Transcripts help us assess your technical and analytical abilities, complementing our interview process in which we also evaluate practical experience and cultural fit. If you choose not to share your transcripts, you will need to provide detailed information regarding your academic performance in relevant courses, including projects and coursework specifics, to ensure we evaluate your academic accomplishments properly. If you do provide academic transcripts, feel free to redact non-technical information (e.g., student ID, dates, non-technical coursework, etc.). Unofficial transcripts obtained online acceptable for this assessment.