Ml Engineer

Acara Solutions; United States; Remote; USD 90,000 - 120,000; Full time

Job summary

Acara Solutions is seeking a highly coding-heavy ML engineer to develop and validate machine-learning models, training pipelines, inference systems, and the supporting infrastructure. You will implement model components, data pipelines, evaluation systems, and numerical methods as part of a productive team.

The role requires strong professional or research experience in machine learning and practical proficiency with Python and coding agents, with opportunities to contribute to reproducible

Qualifications

• A master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
• Strong professional or research experience in machine learning.
• Practical proficiency with Python and coding agents.

Responsibilities

• Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible programmatic workflows using Python and command-line tools.

Skills

Python

Coding agents

Machine learning

Education

Master’s degree or PhD in relevant field

Tools

PyTorch

JAX

NumPy/SciPy

SGLang

vLLM

llama.cpp

Hugging Face Transformers

Hugging Face Tokenizers

Job description

This is a very coding heavy role. Candidates but have experience using coding agents with python in their workflow.

What Youll Work On

1. Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
2. Implement model components, data pipelines, evaluation systems, and numerical methods.
3. Build reproducible programmatic workflows using Python and command-line tools.

Required Qualifications

1. A master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
2. Strong professional or research experience in machine learning.
3. Practical proficiency with Python and coding agents.

Relevant tools may include:

1. PyTorch
2. JAX
3. NumPy and SciPy
4. SGLang
5. vLLM
6. llama.cpp
7. Hugging Face Transformers
8. Hugging Face Tokenizers

Equivalent tools may also be considered when the candidate demonstrates directly relevant depth.

Experience at a well-established technology company, AI laboratory, research organization, or other recognized engineering environment is strongly preferred. Exceptional open-source or academic experience may also qualify.