Machine Learning Engineer

Liquid XR • Los Angeles, CA

3d

About The Position

LiquidXR is an expanding, well-funded early-stage company building a platform to digitize human movement. Its next-generation wearables use proprietary MetalGel sensor technology to capture movement data and feed it to machine-learning-enhanced algorithms and SDKs. The platform is being developed for applications including sports performance, wellness, clinical and healthcare, gaming and XR, and robotics and AI.

The Machine Learning Engineer will develop advanced models that extract meaningful signals from multimodal time-series data. The work focuses on robust, real-time algorithms for noisy, high-frequency sensor inputs, including temporal modeling, representation learning, and inference under real-world constraints. The position is full-time and may be performed remotely or from LiquidXR’s Los Angeles office. Compensation is commensurate with experience and competitive with the market. Occasional domestic and international travel may be required.

Requirements

• 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 or real-time systems.
• Experience with sensor data, such as IMUs.
• Familiarity with sensor-fusion methods, such as Kalman filters and probabilistic models.
• Experience with multimodal or multi-task learning.
• Ability to reason about temporal structure, causality, and latency, and a strong intuition for modeling tradeoffs versus deployment constraints.
• Comfort working with imperfect, real-world data and taking end-to-end ownership.
• BSc or MSc degree in a quantitative field such as computer science, engineering, physics, or applied mathematics.

Nice To Haves

• Exposure to embedded or edge deployment constraints.
• Background in applied domains involving physical systems or human data.

Responsibilities

• Design and implement machine-learning models for time-series and sequential data.
• Develop algorithms that extract structured signals and latent variables from noisy sensor inputs.
• Build and optimize real-time inference pipelines subject to latency and compute constraints.
• Explore and apply temporal convolutional networks, recurrent neural networks, LSTMs, GRUs, and transformer-based sequence models.
• Work on multimodal learning and sensor fusion.
• Replace or augment classical signal-processing pipelines with learned models.
• Design training strategies for windowed and streaming data, weakly labeled or partially observed datasets, and multi-task learning setups.
• Evaluate models using statistical metrics and application-driven performance criteria.
• Collaborate with cross-functional teams to bring models from research to production.
• Take end-to-end ownership of modeling, validation, and deployment.

Benefits

• Employee stock option program.
• Health care benefits, including gold PPO coverage with Blue Shield, starting within 30 days of employment.
• Dental and vision coverage.
• Open PTO.

What This Job Offers

• Job Type: Full-time