Machine Learning Engineer

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