Senior Algorithm Engineer

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.

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.

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.