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Remote Machine Learning Compiler Engineer Jobs in Los Angeles, CA

The Role 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 ...

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Remote Machine Learning Compiler Engineer information

See Los Angeles, CA salary details

$80.8K

$180.4K

$220.9K

How much do remote machine learning compiler engineer jobs pay per year?

As of Sep 2, 2026, the average yearly pay for remote machine learning compiler engineer in Los Angeles, CA is $180,416.00, according to ZipRecruiter salary data. Most workers in this role earn between $154,100.00 and $220,900.00 per year, depending on experience, location, and employer.

What is a remote machine learning compiler engineer?

A Remote Machine Learning Compiler Engineer is a software engineer who specializes in developing and optimizing compilers specifically for machine learning workloads, while working from a remote location. Their primary responsibilities include designing and implementing compiler features that translate machine learning models into efficient code for various hardware platforms, such as CPUs, GPUs, or specialized accelerators. They collaborate closely with machine learning researchers, hardware engineers, and software developers to ensure high performance and compatibility. In addition to strong programming skills, they typically require expertise in compiler theory, machine learning frameworks, and hardware architectures. This role allows for flexible, location-independent work while contributing to cutting-edge AI technologies.

How does a remote machine learning compiler engineer typically collaborate with cross-functional teams to optimize model deployment?

As a Remote Machine Learning Compiler Engineer, you will frequently collaborate with data scientists, hardware engineers, and software developers to ensure that machine learning models are efficiently compiled and deployed on target platforms. Communication often takes place through virtual meetings, code reviews, and shared documentation tools. You'll be responsible for translating research models into optimized code, troubleshooting performance bottlenecks, and integrating feedback from various stakeholders. Effective teamwork is crucial, as the success of deployments often depends on iterative feedback and close alignment with both the ML research and hardware teams.

What are the key skills and qualifications needed to thrive as a remote machine learning compiler engineer, and why are they important?

To thrive as a Remote Machine Learning Compiler Engineer, you need a strong background in computer science, proficiency in programming languages like C++ and Python, and expertise in compiler theory and machine learning frameworks. Familiarity with ML compilers such as TVM or XLA, and experience using version control and CI/CD systems are commonly required, along with a relevant bachelor's or master's degree. Outstanding problem-solving, collaboration, and communication skills are essential for working effectively in distributed teams and across technical domains. These skills and qualities enable the development of efficient, scalable ML solutions that bridge software and hardware, ensuring high performance and innovation.

What is the difference between Remote Machine Learning Compiler Engineer vs Remote Data Scientist?

AspectRemote Machine Learning Compiler EngineerRemote Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Software Engineering, or related fields; knowledge of compiler design and ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in programming, statistics, and data analysis
Work EnvironmentPrimarily software development, compiler optimization, and ML model deploymentData analysis, model building, and interpretation of results
Industry UsageTech companies, AI startups, hardware firms focusing on ML hardware accelerationTech, finance, healthcare, and research organizations

While both roles involve working with machine learning, the Remote Machine Learning Compiler Engineer focuses on developing and optimizing compilers for ML models, whereas the Remote Data Scientist concentrates on analyzing data and building predictive models. The roles share some technical skills but differ in their core responsibilities and work environments.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in Los Angeles, CA?

The most popular types of Machine Learning Compiler Engineer jobs in Los Angeles, CA are:

What cities near Los Angeles, CA are hiring for Remote Machine Learning Compiler Engineer jobs?

Cities near Los Angeles, CA with the most Remote Machine Learning Compiler Engineer job openings:

Machine Learning Engineer

Liquid XR

Los Angeles, CA โ€ข On-site, Remote

Full-time

Medical, Dental, Vision, PTO

Posted 4 days ago


Job description

About Us

LiquidXR is an expanding, well-funded startup 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 (gaming, sports, performance, XR, and more).

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.

The Role

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.


What You'll Do

  • 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

What You'll Bring (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.

Compensation, Benefits, Hours

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.

DISCLAIMER

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. 

LI-DNI