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求人Sila Nanotechnologies

Software Internship - AI & Battery Informatics

Sila Nanotechnologies

Software Internship - AI & Battery Informatics

Sila Nanotechnologies

Alameda, CA

·

On-site

·

Internship

·

1mo ago

報酬

$52,000 - $81,120

必須スキル

Python

NumPy

SciPy

Pandas

Polars

Vaex

Dask

LangGraph

CrewAI

OpenCV

Docker

Kubernetes

Git

About Us

We are Sila, a next-generation battery materials company. Our mission is to power the world’s transition to clean energy. To create this future, our team is building a better lithium-ion battery from the inside out today. We engineer and manufacture ground-breaking battery materials that significantly increase the energy density of batteries, while reducing their size and weight. The result? Smaller more powerful batteries that can unlock innovation in consumer devices and accelerate the mass adoption of electric cars to eliminate our dependence on fossil fuels. We're tackling one of the biggest challenges of our time every day, and together we're redefining what's possible. Are you ready to be a part of a team committed to changing the world?

Who You Are

Are you a code-savvy engineer who thrives on high-performance data and agentic systems? We are looking for an AI-forward, battery science-centric software intern to bridge the gap between electrochemical R&D and production-grade data science. You will complement our Battery Engineering & R&D engineering teams by focusing on the "heavy lifting" of data systems, computer vision, and AI-forward applications. You are someone who views data as a high-performance asset and wants to build the autonomous workflows that will determine how we discover the next generation of energy storage.

Project examples could include designing an AI Agent to browse experimental databases, analyze ECT data, and summarize performance trends. Developing a production CV pipeline that automates fiber/particle quantification in seconds, replacing manual measurement. Or creating the "plumbing" for physics-based models to ingest millions of rows of cycling data via high-performance manipulation.

Responsibilities and Duties

  • Build and deploy autonomous AI agents to automate experimental management, enabling researchers to query, summarize, and reason over complex datasets.

  • Develop automated pipelines for eg. SEM (Scanning Electron Microscopy) and other characterization techniques to automate feature extraction (e.g., particle morphology, particle sizing, feature extraction, and defect detection).

  • Data orchestration and optimization support for modeling teams with build and validate physics-based and ML- models (Transformers, GNNs) to predict battery performance.

  • Design and optimize data pipelines capable of handling massive, high-frequency battery datasets (cycling, modeling) using advanced frameworks for out-of-memory processing.

  • Support the development of backend pipelines, FastAPI tools, and internal dashboards (Hex, React,js) to bring AI-driven insights to a wider audience.

Knowledge and Skill Requirements

  • BSc (or preferably MSc/currently enrolled in a Ph.D. program) in Computer Science, Materials Science, or a related field.

  • Mastery of Python scientific libraries (Num Py, Sci Py, Pandas). Expert-level experience with high-performance frameworks like Polars, Vaex, or Dask for massive datasets.

  • Experience building agentic workflows (e.g., Lang Graph, CrewAI) and utilizing cloud-native AI functions such as Snowflake Cortex.

  • Proficiency in OpenCV or deep learning-based image segmentation (e.g., SAM) for material characterization.

  • Strong proficiency in building production-ready code, including K8s, Docker, and CI/CD pipelines.

  • Basic understanding of Li-ion battery chemistry, materials processing, and electrochemical testing.

  • Proficiency in using AI coding tools (e.g., Claude Code, Copilot) and Git-based collaboration.

Physical Demands and Working Conditions

  • Move long distances and be stationary for extended periods of time.

  • Operate a computer and other office equipment in a fixed location.

  • This role is on-site in our Alameda HQ.

The starting base pay for this role is between $25/hr and $39/hr at the time of posting. The actual base pay depends on many factors, such as education, experience, and skills. Base pay is only one part of Sila’s competitive Total Rewards package that can include benefits, perks, and equity. The base pay range is subject to change and may be modified in the future. This role may also be eligible for overtime.

Working at Sila

We believe that building a diverse team at Sila helps us amplify our individual talents. We are an equal opportunity employer and committed to creating an inclusive environment where good ideas are free to come from anyone. We are proud to celebrate diversity and all qualified applicants are considered for employment without regard to gender, race, sexual orientation, religion, age, disability, national origin, or any other status protected by law.

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Sila Nanotechnologiesについて

Sila Nanotechnologies

Sila Nanotechnologies, Inc. is an American battery manufacturer that produces lithium–silicon batteries using nanoengineered silicon particles. The company creates battery materials to replace traditional graphite anodes with a silicon-dominant composite material to increase energy density.

201-500

従業員数

Alameda

本社所在地

$3.3B

企業価値

レビュー

3.7

33件のレビュー

ワークライフバランス

3.5

報酬

4.2

企業文化

3.7

キャリア

3.6

経営陣

3.4

73%

友人に勧める

良い点

Interesting projects and challenges

Good work-life balance and flexible environment

Competitive compensation and benefits

改善点

Career progression could be clearer

Some organizational bureaucracy

Room for improvement in processes

給与レンジ

38件のデータ

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Mid/L4 · Senior Process Engineer

1件のレポート

$166,400

年収総額

基本給

$128,000

ストック

-

ボーナス

-

$166,400

$166,400

面接体験

48件の面接

難易度

3.3

/ 5

期間

14-28週間

内定率

42%

体験

ポジティブ 65%

普通 19%

ネガティブ 16%

面接プロセス

1

Phone Screen

2

Technical Interview

3

Hiring Manager

4

Team Fit

よくある質問

Technical skills

Past experience

Team collaboration

Problem solving