We are looking for a Senior Algorithm engineer to join a high-velocity R&D team developing advanced Real-Time algorithms for mission-critical defense systems. The role focuses on designing, implementing, and evolving estimation, inference, and decision algorithms that operate under real-world constraints such as uncertainty, latency, partial observability, and adversarial conditions. You will take ownership of core algorithmic components from theoretical formulation through deployment in operational systems.
Responsibilities:
Design, implement, and maintain Real-Time estimation and inference algorithms operating on heterogeneous sensor inputs. Develop and tune probabilistic state-estimation models, including non-linear filters, adaptive models, and prediction mechanisms. Implement uncertainty management, gating, clustering, hypothesis handling, and state lifecycle logic. Integrate AI/ML components where they provide measurable value (classification, anomaly detection, decision support). Own algorithm performance across stability, convergence, latency, and numerical robustness metrics. Translate operational and system -level needs into mathematical formulations and deployable algorithms. Collaborate closely with system engineers and software teams to integrate algorithms into Real-Time command-and-control environments. Analyze operational data, simulations, and edge cases; iteratively refine models based on empirical performance. Participate in algorithm design reviews, trade-off analyses, and performance assessments.
Responsibilities:
Design, implement, and maintain Real-Time estimation and inference algorithms operating on heterogeneous sensor inputs. Develop and tune probabilistic state-estimation models, including non-linear filters, adaptive models, and prediction mechanisms. Implement uncertainty management, gating, clustering, hypothesis handling, and state lifecycle logic. Integrate AI/ML components where they provide measurable value (classification, anomaly detection, decision support). Own algorithm performance across stability, convergence, latency, and numerical robustness metrics. Translate operational and system -level needs into mathematical formulations and deployable algorithms. Collaborate closely with system engineers and software teams to integrate algorithms into Real-Time command-and-control environments. Analyze operational data, simulations, and edge cases; iteratively refine models based on empirical performance. Participate in algorithm design reviews, trade-off analyses, and performance assessments.
Requirements:
MSc or PhD in Electrical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, or Physics.
Strong theoretical and practical background in probabilistic estimation, Bayesian inference, and stochastic systems.
Proven experience developing Real-Time algorithms for complex, sensor-driven systems (defense, aerospace, robotics, automotive, or similar domains).
Excellent foundation in linear algebra, probability theory, estimation methods, and numerical computation.
Hands-on experience implementing production-grade algorithms in Python or similar.
Experience dealing with asynchronous inputs, missing or degraded data, and noisy measurements.
Ability to reason rigorously about algorithm behavior under non-ideal and adversarial conditions.
Comfortable operating in a fast-paced, high-ownership environment with minimal supervision.
Advantages Experience with defense or security command-and-control systems.
Familiarity with advanced estimation, hypothesis management, or density-based modeling techniques.
Background in hybrid model-based and learning-based algorithmic approaches.
Publications, patents, or demonstrable academically rigorous work. Valid Level 3 Security Clearance or eligibility.
MSc or PhD in Electrical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, or Physics.
Strong theoretical and practical background in probabilistic estimation, Bayesian inference, and stochastic systems.
Proven experience developing Real-Time algorithms for complex, sensor-driven systems (defense, aerospace, robotics, automotive, or similar domains).
Excellent foundation in linear algebra, probability theory, estimation methods, and numerical computation.
Hands-on experience implementing production-grade algorithms in Python or similar.
Experience dealing with asynchronous inputs, missing or degraded data, and noisy measurements.
Ability to reason rigorously about algorithm behavior under non-ideal and adversarial conditions.
Comfortable operating in a fast-paced, high-ownership environment with minimal supervision.
Advantages Experience with defense or security command-and-control systems.
Familiarity with advanced estimation, hypothesis management, or density-based modeling techniques.
Background in hybrid model-based and learning-based algorithmic approaches.
Publications, patents, or demonstrable academically rigorous work. Valid Level 3 Security Clearance or eligibility.
This position is open to all candidates.
