Real Time Inference Developer 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

LiquidXR is seeking a Machine Learning Engineer to help build advanced models that turn multimodal time-series sensor data into meaningful signals. In this hybrid role based in Los Angeles, you will focus on robust real-time algorithms that perform reliably with noisy, high-frequency inputs.

The work spans modeling, training strategy, and practical deployment, from research-style experimentation through to production-grade inference 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 with 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 take 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

What you bring

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 the frequency domain
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 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 modeling tradeoffs versus deployment constraints
Comfort working with imperfect, real-world data
End-to-end ownership from modeling through validation to deployment
Self-starter mindset with the ability to catch the vision and run with it
Clear communicator and effective collaborator with cross-functional teams
Ability to manage multiple priorities without sacrificing quality
Detail-oriented approach focused on high-quality and well-documented work
Ownership mindset with full accountability from concept to completion

Tools and technologies

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

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

Location: Los Angeles, CA (hybrid). Employment: Full-time employee position, working remotely or in the Los Angeles office. Compensation: Commensurate with experience and competitive with the market. Travel: Occasional domestic and international travel may be required.

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