# AI/Machine Learning Engineer

**Company**: 3M
**Location**: Maplewood, MN
**Work arrangement**: onsite
**Experience**: entry
**Job type**: full-time
**Salary**: $124,127 - $151,710
**Category**: Engineering
**Industry**: Manufacturing
**Wikidata**: https://www.wikidata.org/wiki/Q159433

**Apply**: https://3m.wd1.myworkdayjobs.com/en-US/Search/job/US-Minnesota-Maplewood/AI-Machine-Learning-Engineer_R01170506?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_56a691e4-a35

## Description

As an AI Software Developer, Early Career, you will join an internal incubator building a new-to-the-world AI architecture and a new technical category. You will work alongside experienced researchers and engineers to convert Python research and development code into scalable production components, primarily in Rust.

Responsibilities:

- Translating well-specified Python research modules into readable, tested, maintainable, and performant Rust components.

- Building and maintaining unit, integration, regression, simulation, behavioral-equivalence, determinism, and reproducibility tests. QA and Production validation.

- Implementing stable interfaces and runtime components within a modular architecture designed to protect proprietary intellectual property.

- Profiling CPU usage, memory, latency, and throughput; partnering with senior developers to improve performance and scalability.

- Supporting concurrent and parallel execution, Python/Rust interoperability, containerization, CI/CD, observability, and deployment automation.

- Helping create components that can support real-time inference, near-real-time reasoning, offline simulations, cloud services, on-premises systems, and embedded or edge environments.

- Working closely with research and engineering teammates to clarify requirements, acceptance criteria, and appropriate tradeoffs between algorithm adaptation and reference fidelity.

- Documenting designs, interfaces, tests, benchmarks, and operating assumptions so components can be safely understood and maintained.

- Learning structured relational, causal, and neural symbolic computation from experienced researchers and applying that knowledge to production software.

- Contributing to a collaborative, low-ego team culture centered on curiosity, disciplined engineering, and responsible handling of confidential intellectual property.

Requirements:

- Bachelor's degree or higher in computer science, computer engineering, electrical engineering, applied mathematics, physics, cognitive science, computational neuroscience, or a related field.

- OR: High School Diploma/GED and five or more years of experience developing, testing, and maintaining software or technology systems.

- Five (5) years programming ability in Python and either C++, Rust, or another systems programming language.

- Three (3) years of core software-engineering practices, including version control, code review, debugging, automated testing, and technical documentation.

Additional qualifications that could help you succeed even further in this role include:

- Ability to understand unfamiliar algorithms, break work into testable components, and collaborate effectively with researchers and senior engineers.

- Hands-on Rust experience or a strong interest and demonstrated ability to learn Rust quickly.

- Experience converting prototypes, research code, simulations, or mathematical algorithms into robust software.

- Coursework or experience in algorithms, data structures, operating systems, concurrency, parallel computing, compilers, graph algorithms, or high-performance computing.

- Exposure to symbolic AI, neural-symbolic systems, cognitive architectures, causal reasoning, analogical reasoning, reinforcement learning, constraint solving, or simulation systems.

- Experience with Python/Rust or Python/C++ interoperability, containers, Kubernetes, Cloud Providers (AWS, GCP, Azure), on-premises systems, CI/CD, databases, graph stores, or messaging systems.

- Experience with profiling, benchmarking, deterministic execution, reproducible experiments, or performance-sensitive software.

- A collaborative, communicative low-ego working style, intellectual curiosity, testing discipline, and sound judgment when handling confidential or access-controlled intellectual property.

## Skills

### Required
- Python
- C++
- Rust
- software engineering
- algorithms
- data structures

### Nice to have
- symbolic AI
- neural-symbolic systems
- cognitive architectures
- causal reasoning
- analogical reasoning
- reinforcement learning
- constraint solving
- simulation systems

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