Sensor Fusion Engineer Jobs

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1d ago

Machine Learning Engineer

Liquid XR Los Angeles, CA

Los Angeles, CA

1d ago

Full time Ai Ml Artificial Intelligence Data Pipeline

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Job Description

Liquid XR is looking for a Machine Learning Engineer to help build advanced models that reveal meaningful signals from multimodal time-series data. The work centers on creating robust real-time algorithms that can handle noisy, high-frequency sensor inputs while fitting practical latency and compute constraints.

Role summary

In this position, you will design end-to-end machine learning solutions for sequential data, including model development, training strategy, evaluation, and productionization with cross-functional teams. The focus includes signal extraction, latent variable learning, and sensor fusion approaches that move beyond purely classical pipelines.

Responsibilities

Design and implement machine learning models for time-series and sequential data
Develop algorithms to extract structured signals and latent variables from noisy sensor inputs
Build and optimize real-time inference pipelines under latency and compute constraints
Apply multi-modal learning and sensor fusion techniques
Replace or augment classical signal processing pipelines with learned models
Design training strategies for windowed and streaming data
Design training strategies for weakly labeled or partially observed datasets
Design training strategies for multi-task learning setups
Evaluate models using both statistical metrics and application-driven performance criteria
Collaborate with cross-functional teams to bring models from research to production
Explore architectures including Temporal convolutional networks (TCNs)
Explore architectures including RNNs, LSTMs, and GRUs
Explore architectures including Transformer-based sequence models

Required qualifications

Strong experience with machine learning for time-series data
Experience with transfer learning and knowledge distillation techniques
Proficiency in Python and PyTorch (or similar frameworks)
Solid understanding of signal processing fundamentals, including filtering, noise, and frequency domain concepts
Experience working with real-world, noisy datasets
Experience building or deploying low-latency, real-time systems
Experience with sensor data (for example, IMUs)
Familiarity with sensor fusion methods such as Kalman filters and probabilistic models
Experience with multi-modal or multi-task learning
Exposure to embedded or edge deployment constraints
Background in applied domains involving physical systems or human data
BSc or MSc in quantitative fields such as computer science, engineering, physics, or applied math
Ability to reason about temporal structure, causality, and latency
Strong intuition for tradeoffs between modeling goals and deployment constraints
Comfort working with imperfect, real-world data
End-to-end ownership from modeling through validation to deployment
Proactive, collaborative, detail-oriented, adaptable, and resilient working style

Technologies

Python
PyTorch
RNNs
LSTMs
GRUs
Transformer-based sequence models
Temporal convolutional networks (TCNs)
Kalman filters

Education

BSc or MSc in quantitative fields

Location and employment

Los Angeles, CA (hybrid) or remote work
Full-time employee position

Compensation and benefits

Compensation commensurate with experience and competitive with the market
Employee stock option program
Health care benefits starting within 30 days of employment (currently gold PPO coverage with Blue Shield, plus dental and vision)
Open PTO company

Travel

Occasional domestic and international travel may be required

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