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Phd Machine Learning Startup Jobs in California (NOW HIRING)

Microsoft

Machine Learning

Mountain View, CA · On-site

$220K - $331K/yr

The MAIST is a startup-like team inside Microsoft AI , created to push the boundaries of AI toward ... Doctorate in Computer Science, Machine Learning, Human-Centered AI or related field AND 2+ year(s ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

$147K - $272K/yr

As part of our machine learning team, you will play a vital role in prototyping foundational ... Preferred Qualifications MS/PhD in computer vision, electrical, optical or computer engineering or ...

Apple

Machine Learning Engineer, Proactive

Cupertino, CA

$212K - $318K/yr

... PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field. At least 2 years of experience in various state-of-the-art techniques related to LLM fine-tuning in 1 or more ...

EarnIn

Head of Machine Learning

Mountain View, CA · Hybrid

$477K - $583K/yr

... fast-paced startup environments. This leader should have a strong coding foundation, deep ... Define and own the machine learning roadmap in alignment with business goals. * Lead the ML ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

They are seeking a Machine Learning Engineer to design and develop scalable training pipelines for ... paced startup environment, and able to demonstrate strong ownership and urgency in execution.

Motorola

Machine Learning Engineer

Los Angeles, CA

Minimum of 3 years of experience in machine learning, with demonstrated application to real-world problems; 1 year of machine learning experience with a PhD. * Strong foundation in supervised and ...

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Phd Machine Learning Startup information

What are some common challenges faced by PhD-level professionals working in machine learning startups?

PhD-level professionals in machine learning startups often encounter challenges such as balancing research innovation with the need for rapid product development. Unlike academia, startups prioritize practical solutions that fit tight deadlines and resource constraints. Team members typically wear multiple hats and collaborate closely with engineers, product managers, and business stakeholders, requiring strong communication skills and adaptability. Additionally, translating cutting-edge research into scalable, real-world applications can be both intellectually rewarding and demanding.

What do PhD holders in Machine Learning do at startups?

PhD holders in Machine Learning at startups typically lead research and development efforts to create innovative algorithms and models that solve real-world problems. They often work on designing and implementing advanced machine learning solutions, analyzing large datasets, and collaborating with product and engineering teams to bring research ideas to production. Their expertise helps startups stay competitive by driving technological advancements and fostering a culture of innovation.

What are the key skills and qualifications needed to thrive as a PhD-level Machine Learning professional in a startup environment, and why are they important?

To excel as a PhD-level Machine Learning professional at a startup, you need advanced expertise in machine learning algorithms, statistical modeling, and a doctoral degree in a related field. Experience with Python, TensorFlow, PyTorch, and version control systems, along with a strong publication record, is typically expected. Initiative, adaptability, and excellent problem-solving and communication abilities are crucial soft skills in the fast-paced startup setting. These competencies enable rapid innovation, effective team collaboration, and successful deployment of machine learning solutions under resource constraints.
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Phd Machine Learning Startup jobs near you

Machine Learning Engineer - Robot Manipulation

Maven Robotics

San Francisco, CA • On-site

Other

Posted 6 days ago

Job description

Company Overview

Maven Robotics is building the world's leading general-purpose AI robots.

We are currently operating in stealth and are growing the world's best team in AI robotics. We are looking for self-starters that are the world's best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.

Role Description

We are looking to recruit an exceptional Machine Learning Engineer - Robot Manipulation to design, implement, test, and deploy robot manipulation algorithms that enable assembly and material movement tasks.

In this role you will:

  • Design and implement machine learning algorithms, with a focus on reinforcement learning (RL) and imitation learning (IL), to enable robotic manipulators to perform complex tasks in dynamic environments.
  • Translate high-level objectives into machine learning problems and deploy robust, scalable models to real-world robotic systems.
  • Integrate your ML solutions into existing robotics workflows, ensuring that models are performant in both simulated and real-world settings.
  • Drive innovation by incorporating the latest research in machine learning into practical applications that push the boundaries of robotic manipulation.
  • Take ownership of critical ML projects, seeing them through from conception to successful deployment.
  • Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.
Qualifications

Must-have:

  • MS or PhD in machine learning, computer science, robotics, or a related field.
  • Strong practical experience in training and deploying machine learning models for real-world applications.
  • Deep understanding of reinforcement learning (RL) and imitation learning (IL) and their application to robotics.
  • Proficiency in programming languages and tools commonly used in machine learning (e.g., Python, PyTorch).
  • Experience with data collection, preprocessing, and management in the context of training ML models.
  • Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
  • Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:

  • Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
  • Experience in:
    • Designing and implementing reward functions for complex manipulation tasks.
    • Developing models that can handle noisy, incomplete, or sparse data.
    • Deployment of ML models to edge devices for real-time inference.
    • Accelerating ML training processes using GPU, TPU, or other HW accelerators.
    • Using reinforcement learning frameworks, e.g. Stable Baselines, RLlib, or similar.
  • General knowledge of robotics principles, including kinematics, dynamics, and control.
  • Publications or contributions to the machine learning community, particularly in areas related to robotics or reinforcement learning.

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