[Hiring] Machine Learning Engineer @Liquid XR

Role Description

We are seeking a Machine Learning Engineer to develop advanced models for extracting meaningful signals from multimodal time-series data. This role focuses on building robust, real-time algorithms that operate on noisy, high-frequency sensor inputs. You will work on problems involving temporal modeling, representation learning, and inference under real-world constraints.

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 and apply architectures such as:
  • Temporal convolutional networks (TCNs)
  • 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 and streaming data
  • Weakly labeled or partially observed datasets
  • 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.

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 (filtering, noise, frequency domain).
• Experience working with real-world, noisy datasets.
• Experience building or deploying low-latency / real-time systems.
• Experience with sensor data (e.g., IMUs).
• Familiarity with sensor fusion methods (e.g., Kalman filters, 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 degree in quantitative fields (e.g., computer science, engineering, physics, applied math).

Who You Are

• Ability to reason about temporal structure, causality, and latency.
• Strong intuition for modeling tradeoffs vs. deployment constraints.
• Comfort working with imperfect, real-world data.
• End-to-end ownership: from modeling to validation to deployment.
• An Owner: You possess a powerful ownership mindset and take full accountability for your projects from concept to completion.
• A Proactive Driver: You are a self-starter who can "catch the vision and run with it." You thrive with autonomy and are skilled at moving projects forward with minimal oversight.
• A Team Player: You are a natural collaborator who communicates clearly and works effectively with cross-functional teams to achieve shared goals.
• Adaptable and Resilient: You excel at managing multiple priorities without sacrificing quality. You see the challenges of a startup environment as opportunities.
• Detail-Oriented: You have a keen eye for detail and are committed to producing high-quality, well-documented work.

Benefits

• This is a full-time employee position, working remotely or in our Los Angeles office.
• Compensation will be commensurate with experience and will be competitive with the market.
• You will also participate in the employee stock option program.
• You will be provided health care benefits (currently, gold PPO coverage with Blue Shield, as well as dental and vision) starting within 30 days of employment.
• We are an open PTO company.
• Occasional travel may be required domestically and internationally.