Modal

Member of Technical Staff - ML Performance

New York, New York, United States

$150,000 – $270,000Compensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
AI & Machine Learning, Enterprise SoftwareIndustries

Requirements

Candidates should have at least 5 years of experience writing high-quality, high-performance code. Experience with high-level ML frameworks and inference engines such as torch, vLLM, or TensorRT is required. Familiarity with Nvidia GPU architecture and CUDA is essential, along with experience in ML performance engineering, particularly in boosting GPU performance and debugging issues. Familiarity with low-level operating system foundations like the Linux kernel, file systems, and containers is a plus. Candidates must be able to work in-person at the NYC, San Francisco, or Stockholm office.

Responsibilities

The Member of Technical Staff will focus on enhancing the performance of ML systems at scale. They will contribute to open-source projects and Modal’s container runtime, aiming to improve throughput and reduce latency for language and diffusion models.

Skills

torch
CUDA
vLLM
TensorRT
GPU Architecture
Performance Engineering
Linux Kernel
File Systems
Containers

Modal

Employee training and skill development platform

About Modal

Modal Learning focuses on improving employee performance through skill development for businesses. Their main product, the Modal Mastery Platform, uses active learning techniques, including live cohort sessions, labs, and one-on-one coaching, to help employees engage with the material effectively. Unlike competitors, Modal Learning offers a subscription model that provides structured eight-week training programs, aligning skill development with organizational goals. The company's goal is to empower employees and help organizations retain talent by providing clear career development paths.

Key Metrics

San Francisco, CaliforniaHeadquarters
2021Year Founded
$30.8MTotal Funding
EARLY_VCCompany Stage
Consulting, EducationIndustries
51-200Employees

Risks

Competition from established players and emerging startups could dilute Modal's market share.
Focus on data and AI may limit appeal to companies seeking broader skill development.
Economic downturns could reduce corporate spending on employee training, impacting revenue.

Differentiation

Modal offers personalized technical skills training with on-demand coaching.
The platform uses cohort-based learning to enhance engagement and retention.
Modal's strategic skills planning aligns training with business goals.

Upsides

Increased demand for personalized learning experiences boosts Modal's market potential.
Growing emphasis on data and AI skills aligns with Modal's course offerings.
Subscription model provides steady revenue and predictable growth for Modal.

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