Creative ML Technologist at Parallel Domain

Vancouver, British Columbia, Canada

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Not SpecifiedCompensation

Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Autonomous Systems, SimulationIndustries

Requirements

ComfyUI experience: Demonstrable experience building multi-step ComfyUI graphs and compiling/deploying open-sourced projects through wrappers, including building from source, compiling as needed, and integrating for readable, reusable workflows
Creative and technical skill: Strong creative sensibilities combined with practical, hands-on machine learning skills
Python and ML proficiency: Solid Python and PyTorch fundamentals with hands-on experience, including tensor shapes, dtypes, autocast or no-grad, memory-aware batching, and devices
Workflow design and evaluation: Experience assembling perception and generative workflows for visual tasks and evaluating tradeoffs with simple, meaningful metrics
VFX/Games production experience: Professional experience in VFX or game development with practical knowledge of content creation pipelines and cross-functional collaboration
Clear communication: Ability to explain complex findings clearly and effectively to both creative and engineering stakeholders
What will help you stand out
Training experience: Experience training small adapters, such as LoRAs, to improve workflow performance or quality
ComfyUI portfolio: A portfolio with one or more original ComfyUI nodes or wrappers and a short write-up of design decisions
Generative editing quality: Examples of generative editing or scene adjustments delivering high accuracy and visual fidelity, with speed gains a plus

Responsibilities

Compose workflows: Run focused experiments answering product questions using ComfyUI graphs combining perception and generative steps
Measure and compare: Produce side-by-side comparisons with qualitative frames and meaningful metrics to guide decision-making
Custom nodes and integrations: Develop, implement, and deploy custom ComfyUI nodes for novel functionality and create wrappers for new open-source models
Document and guide: Document inputs, controls, expected artifacts, and typical failure modes in concise how-to notes and manifests for others to rerun and extend
Collaborate and iterate: Partner closely with creative users to refine/validate visual output and work with engineering to hand off proven workflows for scaling

Skills

ComfyUI

Generative Models

Perception Models

Custom Nodes

ML Workflows

Generative Editing

Model Synthesis

AI Prototyping

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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