Sensor Fusion Data Jobs

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Machine Learning Engineer

Liquid XR Los Angeles, CA

Los Angeles, CA

Today

Full time Ai Ml Artificial Intelligence Data Pipeline

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Job Description

Liquid XR is hiring a Machine Learning Engineer to build advanced models that uncover meaningful signals from multimodal time-series sensor data. This hybrid role in Los Angeles, CA focuses on robust real-time algorithms designed for noisy, high-frequency inputs, with an emphasis on taking models from research to production under latency and compute constraints.

What you’ll do

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 that respect latency and compute constraints.
Work on multi-modal learning and sensor fusion.
Replace or augment classical signal processing pipelines with learned models.
Create training strategies for windowed and streaming data.
Develop training approaches for weakly labeled or partially observed datasets.
Design and evaluate multi-task learning setups.
Evaluate models using statistical metrics and application-driven performance criteria.
Collaborate with cross-functional teams to move models from research to production.
Explore and apply sequence model architectures including Temporal convolutional networks (TCNs), RNNs / LSTMs / GRUs, and Transformer-based sequence models.

What you bring

Strong experience with machine learning for time-series data.
Experience with transfer learning and knowledge distillation.
Proficiency in Python and PyTorch (or similar frameworks).
Solid understanding of signal processing fundamentals including filtering, noise, and the frequency domain.
Experience working with real-world, noisy datasets.
Experience building or deploying low-latency / real-time systems.
Experience with sensor data such as 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.
Ability to reason about temporal structure, causality, and latency.
Strong intuition for modeling tradeoffs versus deployment constraints.
Comfort working with imperfect, real-world data.
End-to-end ownership from modeling to validation to deployment.
BSc or MSc in quantitative fields (examples: computer science, engineering, physics, applied math).
Team-oriented mindset and clear communication across cross-functional teams.
Proactive, adaptable, resilient approach; ability to manage multiple priorities.
Detail-oriented and committed to high-quality, well-documented work.
Ownership mindset with full accountability from concept to completion.

Benefits

Employee stock option program.
Health care benefits (currently, gold PPO coverage with Blue Shield) plus dental and vision, starting within 30 days of employment.
Open PTO company policy.

Role details

Full-time employee position, working remotely or in our Los Angeles office.
Compensation will be commensurate with experience and competitive with the market.
Occasional travel may be required domestically and internationally.

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

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