Junior Machine Learning Software Engineer, Research
Jobright.ai
Junior Machine Learning Software Engineer, Research Join to apply for the Junior Machine Learning Software Engineer, Research role at Jobright.ai Junior Machine Learning Software Engineer, Research 1 day ago Be among the first 25 applicants Join to apply for the Junior Machine Learning Software Engineer, Research role at Jobright.ai Get AI-powered advice on this job and more exclusive features. Jobright is an AI-powered career platform that helps job seekers discover the top opportunities in the US. We are NOT a staffing agency. Jobright does not hire directly for these positions. We connect you with verified openings from employers you can trust. 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.
- 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.
- 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.
Vacancy posted 2 days ago
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