Remote Machine Learning Quant Jobs (NOW HIRING)

LiquidXR is an expanding, well-funded early-stage company building a platform to digitize human movement. We are creating next-gen wearables using proprietary MetalGel sensor technology, capturing and feeding movement data to our machine learning-enhanced algorithms and SDKs, which connect to any modern computer or development environment. We are partnered with several high-quality companies co-developing products using our tech, and we are advancing our platform to enable all types of body movement data capture and analysis across multiple areas of use (sports performance, wellness, clinical/healthcare, gaming/XR, and robotics/AI). 

Our tight knit hardware and software team is comprised of experts in product and UX development, biomechanics, algorithms and machine learning, software platform and experience development, electronic engineering, and soft goods industrial design. Individually and collectively, this is a team who gets things done and among us, countless products have been launched worldwide. We are passionate about creating a transformative platform and we are fortunate to work on cool products using our tech along the way. 

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.

• 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

• 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)

• 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

• Someone with the ability to reason about temporal structure, causality, and latency

• Have strong intuition for modeling tradeoffs vs. deployment constraints

• Comfortable working with imperfect, real-world data

• Have end-to-end ownership, from modeling to validation to deployment

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. 

We are an affirmative action, equal opportunity employer. Our employment decisions are made without regard to race, color, religion, gender, gender identity, national origin, age, disability, marital status, veteran or military status, or any other legally protected status. 

In accordance with the ADA, employees must perform the essential duties and responsibilities efficiently and accurately, with or without reasonable accommodation. The above statements are intended to describe the general nature and level of work being performed by employees assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and/or skills required of all personnel so classified. 

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