Head of Machine Learning at Parallel Domain

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, SoftwareIndustries

Requirements

Candidates should possess 7+ years of industry machine learning experience and 2+ years of management experience, with a proven track record of leading and mentoring high-performing machine learning teams. They require a deep understanding of machine learning principles, algorithms, and frameworks, as well as strong communication skills and the ability to translate business objectives into technical goals. Hands-on experience in developing and deploying production-level machine learning models is beneficial, and familiarity with academic research in 3D reconstruction, generative AI, or related fields can help candidates stand out.

Responsibilities

The Head of Machine Learning will lead and inspire a team of machine learning engineers and researchers, fostering a culture of innovation and excellence. They will define the technical roadmap for the team, aligning with company objectives and product vision, and manage project timelines and resource allocation to ensure successful execution. This role involves collaborating with cross-functional teams to drive product development and deployment, recruiting and retaining top talent, and staying ahead of the latest advancements in machine learning to inform the team's direction. Additionally, the Head of Machine Learning will ensure delivery of cutting-edge machine learning algorithms for 3D reconstruction and generative AI, shaping the future of the company's products and services.

Skills

Machine Learning

Generative AI

3D Reconstruction

Technical Leadership

Team Leadership

Project Management

Algorithm Development

Parallel Domain

Synthetic data solutions for autonomous systems

About Parallel Domain

Parallel Domain provides synthetic data solutions aimed at speeding up the development of autonomous systems, such as self-driving cars and delivery drones. The company generates synthetic labeled datasets, simulation environments, and controllable sensor feeds, which help teams in perception, machine learning, and data operations to develop, train, and test their algorithms in a safe and efficient manner. Unlike competitors, Parallel Domain offers a user-friendly API that allows clients to easily connect and access their synthetic data, reducing the need for extensive real-world testing. The goal of Parallel Domain is to streamline the development process for autonomous technologies, making it faster and more cost-effective.

Palo Alto, CaliforniaHeadquarters

2017Year Founded

$42.7MTotal Funding

SERIES_BCompany Stage

Robotics & Automation, Enterprise Software, AI & Machine LearningIndustries

11-50Employees

Benefits

Health Insurance

Paid Vacation

Paid Parental Leave

Hybrid Work Options

Professional Development Budget

Flexible Work Hours

Risks

Increased competition from companies like DataMesh in digital twin offerings.

Rapid advancement of generative AI could introduce new market entrants.

Reliance on synthetic data may face scrutiny if real-world testing becomes feasible.

Differentiation

Parallel Domain offers a robust API for synthetic data generation.

PD Replica creates high-fidelity digital twins for autonomous vehicle testing.

Reactor engine uses generative AI for diverse, high-quality synthetic datasets.

Upsides

Increased demand for synthetic data in autonomous vehicle testing.

Growing interest in generative AI technologies for synthetic datasets.

Recent $30 million Series B funding boosts financial stability.

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