ML Engineer (perception & state estimation)

PassFort; Cambridge (MA); Hybrid; USD 90,000 - 120,000; Full time

Job summary

PassFort is seeking a Machine Learning Engineer in Cambridge to join the Perception Team. This hybrid role involves designing algorithms that transform sensor data into a symbolic world model.

Ideal candidates have strong knowledge in probabilistic machine learning and practical experience with sensor fusion techniques, coding in Python and C++. Join us in building innovative solutions for robotic systems!

Qualifications

• Experience with probabilistic machine learning, including Bayesian inference and state estimation.
• Experience in using modern ML frameworks and computer vision libraries with sensor data.
• Hands-on experience in sensor fusion and real-world robotic systems.

Responsibilities

• Design and implement algorithms for multi-sensor fusion.
• Build models for object detection and classification.
• Develop the Knowledge Manager for logical relationships.

Skills

Probabilistic machine learning

Sensor fusion techniques

Python programming

C++ programming

Computer vision libraries (OpenCV)

Tools

PyTorch

Robot Operating System (ROS 2)

Job description

ML Engineer (perception & state estimation)

Department: Perception

Job Type: Full-time

Location: Cambridge

Modality: Hybrid

Start Date: 19/01/2026

We are looking for a Machine Learning Engineer to join our Perception Team. You will build the core perception and reasoning engine for our flagship multi-agent system. This role is responsible for architecting the software that transforms raw, noisy sensor data into a rich, symbolic world model. This team will develop and implement the algorithms for managing perception inputs and maintaining a knowledge manager based on such inputs.

Who we are

About the Role: You'll form part of the Perception Team. This team unlocks the mastermind’s understanding and reasoning about its environment.

This is an on-site position; the successful candidate will be expected to work from the office at least 3 days a week.

What you’ll get to do

• Multi-Sensor Fusion: Design and implement algorithms that manage the fusion of heterogeneous sensor streams (e.g., EO/IR, LiDAR, and neuromorphic cameras) into a single, coherent picture of the world.
• Object Recognition: Build and deploy models for real-time object detection, classification, and tracking, transforming raw data into structured, classified objects with unique IDs and states.
• World Modeling: Develop the Knowledge Manager, the central repository for abstract and symbolic world knowledge. Responsible for inferring the logical relationships between objects and agents.
• Probabilistic State Estimation: Implement and maintain the belief state over the environment, a core component of a knowledge manager.
• Goal Inference: Create the logic that translates high-level user commands into the formal, predicate-based goal states.
• API Collaboration: Work closely with the Systems and Behaviour teams to define and refine APIs.

What we’d like to see

• A strong theoretical foundation and practical experience in probabilistic machine learning (e.g., Bayesian inference, Gaussian Processes, state estimation filters like EKFs/UKFs).
• Demonstrable experience with modern ML frameworks (PyTorch preferred) and computer vision libraries (OpenCV) applied to real-world sensor data.
• Hands‑on experience with sensor fusion techniques for combining data from sources like cameras and LiDAR.
• Production‑quality coding skills in both Python and C++.

What will set you apart

• Proven experience developing and deploying software for real-world robotic systems (e.g., UAVs, UGVs).
• Deep expertise in sensor fusion techniques, particularly with state estimation filters like EKF, for tracking and localization.
• Hands‑on experience with the Robot Operating System (ROS 2) and an understanding of the underlying DDS middleware and its QoS settings.
• Practical experience in multi‑agent reinforcement learning (MARL), planning under uncertainty, or collaborative robotics.
• Familiarity with high‑fidelity simulation environments for robotics, especially NVIDIA Isaac Lab.
• Familiarity with the challenges of real‑time systems, including managing latency, ensuring deterministic timing (e.g., PTP), and maintaining performance on degraded communication links.
• Experience with knowledge representation, logical inference, or symbolic reasoning systems.