Real-Time Time-Series ML Engineer (Remote)

LiquidXR; Los Angeles (CA); Hybrid; USD 130,000 - 190,000; Full time

Benefits offered by this job

Health care benefits

Employee stock option program

Open PTO policy

Travel opportunities

Job summary

LiquidXR in Los Angeles is seeking a Machine Learning Engineer to develop real-time models for multimodal sensor data from next-gen wearables. You will build robust time-series algorithms that operate under noise and latency constraints, enabling production-ready inference.

The ideal candidate has strong experience in time-series ML, transfer learning, and edge deployment, with a track record of delivering scalable ML for real-world datasets.

Qualifications

• Strong experience with ML 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 (filtering, noise, frequency domain).
• Experience working with real-world, noisy datasets and low-latency / real-time systems.
• Experience with sensor data (e.g., IMUs) and sensor fusion methods.

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 with latency and compute constraints.
• Explore architectures such as Temporal convolutional networks, RNNs/LSTMs/GRUs, Transformer-based sequence models.
• Work on multi-modal learning and sensor fusion.
• Replace or augment classical signal processing pipelines with learned models.
• Design training strategies for windowed/ streaming data, weakly labeled datasets, and multi-task setups.
• Evaluate models using statistical metrics and application-driven performance criteria.
• Collaborate with cross-functional teams to bring models from research to production.

Skills

ML for time-series

Transfer learning

Knowledge distillation

Python & PyTorch

Real-time / low-latency

Sensor data (IMUs)

Sensor fusion

Multi-modal / multi-task learning

Edge deployment constraints

Temporal modeling

Education

BSc or MSc in quantitative fields

Tools

Python

PyTorch

Job description

LiquidXR in Los Angeles is seeking a Machine Learning Engineer to develop real-time models for multimodal sensor data from next-gen wearables. You will build robust time-series algorithms that operate under noise and latency constraints, enabling production-ready inference.

The ideal candidate has strong experience in time-series ML, transfer learning, and edge deployment, with a track record of delivering scalable ML for real-world datasets.