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Phd Machine Learning Jobs in Rancho Palos Verdes, CA

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

PhD degree in computer science, engineering, or mathematics * 3-5 years of relevant experience in building deep learning solutions for computer vision problems * Hands-on experience with ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

PhD degree in computer science, engineering, or mathematics * 3-5 years of relevant experience in building deep learning solutions for computer vision problems * Hands-on experience with ...

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Phd Machine Learning information

See Rancho Palos Verdes, CA salary details

$14

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How much do phd machine learning jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for phd machine learning in Rancho Palos Verdes, CA is $24.48, according to ZipRecruiter salary data. Most workers in this role earn between $21.15 and $27.36 per hour, depending on experience, location, and employer.

What is a PhD in machine learning?

A PhD in Machine Learning is an advanced doctoral degree focused on developing new algorithms, theories, and applications in the field of machine learning. Graduates typically conduct original research, contribute to academic publications, and often specialize in areas like deep learning, reinforcement learning, or probabilistic modeling. This degree prepares individuals for careers in academia, industry research labs, or leadership roles in tech companies. The program usually involves coursework, comprehensive exams, and the completion of a dissertation based on novel research.

What are the key skills and qualifications needed to thrive as a PhD-level machine learning professional?

To thrive as a PhD-level Machine Learning professional, you need deep expertise in mathematics, statistics, computer science, and advanced machine learning algorithms, typically supported by a doctoral degree. Proficiency with programming languages like Python or R, machine learning frameworks such as TensorFlow or PyTorch, and experience with large-scale data systems are essential. Strong problem-solving skills, critical thinking, and effective communication set outstanding candidates apart by enabling them to tackle complex research challenges and collaborate across teams. These skills and qualities are crucial for driving innovation, publishing research, and developing impactful machine learning solutions.

What are some common challenges faced by PhD-level professionals in machine learning when transitioning from academia to industry roles?

PhD graduates in machine learning often encounter challenges such as adapting to faster-paced project timelines, aligning research with business objectives, and collaborating in multidisciplinary teams. Unlike academia, where projects can be exploratory and long-term, industry roles usually require actionable results within shorter deadlines. Additionally, communicating complex technical ideas to non-technical stakeholders and prioritizing practical solutions over theoretical novelty are key adjustments. However, these challenges also present opportunities for professional growth and broader impact.

What is the difference between Phd Machine Learning vs Data Scientist?

AspectPhd Machine LearningData Scientist
Required CredentialsPhD in Computer Science, AI, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch labs, academia, R&D departmentsBusiness, tech companies, analytics teams
Industry UsageResearch-focused roles, advanced algorithm developmentData analysis, model building, business insights
Common Search/ComparisonYesYes

While both roles involve working with data and algorithms, a Phd Machine Learning typically focuses on research, developing new models, and theoretical work, often in academic or R&D settings. A Data Scientist applies these techniques to solve practical business problems, analyze data, and generate insights in industry environments.

What cities near Rancho Palos Verdes, CA are hiring for Phd Machine Learning jobs? Cities near Rancho Palos Verdes, CA with the most Phd Machine Learning job openings:
Phd Machine Learning jobs near you
Infographic showing various Phd Machine Learning job openings in Rancho Palos Verdes, CA as of June 2026, with employment types broken down into 2% As Needed, 32% Full Time, 62% Part Time, 2% Temporary, and 2% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $50,921 per year, or $24.5 per hour.

Principal Machine Learning Researcher (Physical AI)

Freeform

Los Angeles, CA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 25 days ago

Job description

PRINCIPAL MACHINE LEARNING RESEARCHER (PHYSICAL AI)
Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts at the speed of human ideation. By treating manufacturing as a single integrated system, we unlock a new era of innovation where complex hardware is designed, built, and scaled without limits.
This architecture enables continuous generation of petabyte-scale, high-fidelity data capturing the physics of metal printing - from in-situ process signals and machine state to geometry and material outcomes. Each factory node contributes to a growing learning system that improves modeling accuracy, control performance, yield, and scalability over time.
Freeform is hiring a Principal Machine Learning Researcher to lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system. The role focuses on developing machine learning methods that integrate large-scale physical data with physics-based simulation and embedding these models into closed-loop control and autonomy frameworks. Work includes modeling relationships between process inputs, geometry, and machine state to predict thermal, mechanical, and geometric outcomes during printing, using hybrid physics-ML approaches and multi-modal in-situ data.
Research is validated against physical outcomes and deployed into production systems, where improvements directly impact stability, yield, throughput, and capability across an expanding fleet of manufacturing nodes. Your work will have a direct and meaningful impact on how frontier technologies are designed and produced at scale.
Responsibilities:
  • Design and develop machine learning models for complex, multi-physics manufacturing processes.
  • Develop hybrid modeling approaches that combine first-principles physics with data-driven learning.
  • Lead the formulation of learning-based models used for prediction and control in production-scale metal additive manufacturing systems.
  • Develop methods to learn from large-scale, high-dimensional in-situ sensor data collected during printing.
  • Design unsupervised and self-supervised learning techniques to correlate process signals with part quality, geometry, and performance.
  • Develop models that link process parameters, geometry, and machine state to thermal and mechanical outcomes.
  • Integrate learned models with physics-based simulation and digital twin frameworks.
  • Contribute to the design of closed-loop control and autonomy systems that operate in real time on production hardware.
  • Develop learning-based approaches for machine health monitoring, anomaly detection, and system diagnostics.
  • Guide the integration of machine learning models into production software and manufacturing workflows.
  • Help define research direction and technical standards for machine learning applied to physical systems within the organization.

Basic Qualifications:
  • 5+ years of experience in machine learning, applied research, or related technical fields or a PhD in machine learning, applied mathematics, physics, robotics, controls, or a closely related discipline.
  • Strong foundations in machine learning applied to physical systems, modeling, or control.
  • Proficiency in Python and at least one systems-level programming language (C/C++ preferred).
  • Experience working with large-scale, noisy, real-world datasets.

Nice to Have:
  • MS or PhD in applied mathematics, physics, robotics, controls, materials science, or a related discipline.
  • Experience with hybrid physics-ML models, digital twins, or simulation-in-the-loop learning.
  • Background in autonomy, robotics, model predictive control, or reinforcement learning for physical systems.
  • Experience with image-based or sensor-based inference in industrial or scientific settings.
  • Familiarity with computational geometry or geometric modeling.
  • Comfort working across theory, experimentation, and deployment in tightly coupled systems.
  • Ability to reason from first principles and translate theory into working models and systems.

Location:
  • Based in Hawthorne, our vertically integrated facility brings technology development, R&D, and production together under one roof. We operate at the center of LA's deep tech ecosystem, surrounded by some of the most ambitious hardware innovation happening anywhere in the country.
  • Our fast-paced, cross-functional environment is built on close collaboration, and as such, this role requires full-time onsite presence (five days a week), with very limited exceptions.

What We Offer:
  • We have an inclusive and diverse culture that values collaboration, learning, and making deliberate data-driven decisions.
  • We offer a unique opportunity to be an early and integral member of a rapidly growing company that is scaling a world-changing technology.
  • Benefits
    • Significant stock option packages
    • 100% employer-paid Medical, Dental, and Vision insurance (premium PPO and HMO options)
    • Life insurance
    • Traditional and Roth 401(k)
    • Relocation assistance provided
    • Paid vacation, sick leave, and company holidays
    • Generous Paid Parental Leave and extended transition back to work for the birthing parent
    • Free daily catered lunch and dinner, and fully stocked kitchenette
    • Casual dress, flexible work hours, and regular catered team building events
  • Compensation
    • As a growing company, the salary range is intentionally wide as we determine the most appropriate package for each individual taking into consideration years of experience, educational background, and unique skills and abilities as demonstrated throughout the interview process. Our intent is to offer a salary that is commensurate for the company's current stage of development and allows the employee to grow and develop within a role.
    • In addition to the significant stock option package, the estimated salary range for this role is $200,000-$400,000. However is this a unique position with outsized impact for the right game-changing hire, so we will consider compensation outside of this range on a case-by-case basis.
  • Freeform is an Equal Opportunity Employer that values diversity; employment with Freeform is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

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