Junior Machine Learning Software Engineer, Research

Jobright.ai

New York (NY)

On-site

USD 93,144 - 180,000

Full time

14 days+

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Benefits offered by this job

Medical insurance

Vision insurance

401(k)

Job summary

A leading AI technology firm in New York, NY is looking for a Junior Machine Learning Software Engineer to develop models addressing real-world challenges in physics and engineering. The ideal candidate should have a strong background in machine learning, excellent problem-solving skills, and a relevant graduate degree. This position offers a full-time employment opportunity with competitive salary and benefits.

Qualifications

Enthusiasm for machine learning solutions and software development.
Ability to work autonomously across various domains.
Record of experience in scientific computing or high-performance computing.

Responsibilities

Work closely with researchers to deliver effective models.
Design and optimize machine learning models for efficiency.
Explore and implement distributed training architectures.

Skills

Machine learning solutions development

Collaboration and communication skills

Problem-solving skills

Scientific computing

High-performance computing

Education

MSc or PhD in relevant field

Tools

Python

AWS

Docker

Kubernetes

Job description

Junior Machine Learning Software Engineer, Research

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Job Summary:

PhysicsX is building an AI-driven platform to massively accelerate physics simulations and unlock new possibilities in engineering. They are seeking a capable and enthusiastic machine learning software engineer to join their research team, focusing on developing machine learning applications for real-world physics and engineering challenges.

Responsibilities:

• Work closely with our research scientists and simulation engineers to build and deliver models that address real-world physics and engineering problems.

• Design, build and optimize machine learning models with a focus on scalability and efficiency in our application domain.

• Transform prototype model implementations to robust and optimized implementations.

• Implement distributed training architectures (e.g., data parallelism, parameter server, etc.) for multi-node/multi-GPU training and explore federated learning capacity using cloud (e.g., AWS, Azure, GCP) and on-premise services.

• Work with research scientists to design, build and scale foundation models for science and engineering; helping to scale and optimize model training to large data and multi-GPU cloud compute.

• Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success.

• Own Research work-streams at different levels, depending on seniority.

• Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.

• Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products.

• Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor.

Qualifications:

Required:

• Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.

• Ability to work autonomously and scope and effectively deliver projects across a variety of domains.

• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.

• Excellent collaboration and communication skills — with teams and customers alike.

• MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following: Scientific computing; High-performance computing (CPU / GPU clusters); Parallelised / distributed training for large / foundation models.