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求人Tesla

Staff Site Reliability Engineer, Energy Software

Tesla

Staff Site Reliability Engineer, Energy Software

Tesla

Palo Alto, California

·

On-site

·

Full-time

·

Today

必須スキル

AWS

Kubernetes

Rust

Terraform

Linux

PostgreSQL

Kafka

Scala

What To Expect
Tesla is looking for a Site Reliability Engineer to build, enhance, and scale the infrastructure that underpins our Energy IoT applications. These applications provide real-time monitoring, optimization, and control for Tesla’s industry-leading energy products, including Powerwall, Megapack, Solar Roof, Supercharger, Wall Connector, Autobidder, and Virtual Power Plants.

We are a high-impact team that values curiosity, learning, mentorship, open discourse, and making disciplined decisions by weighing trade-offs. Our work supports over 50 engineers and directly affects millions of customers.

If you enjoy thinking in systems and tackling challenges related to the availability, reliability, scalability, and security of distributed software, this role is for you.

You’ll work with and deepen your expertise in Linux, Networking, Kubernetes, on-premises data centers, AWS, Terraform, Prometheus, Helm, GitHub Actions, PostgreSQL, CloudNativePG, Kafka, InfluxDB, Scala, and Rust.

Join us in accelerating the world’s transition to sustainable energy.

What You'll Do

  • Envision and implement changes that improve system reliability
  • Conduct deep investigations into new technologies and resolve unexpected issues that arise during operation
  • Provide guidance on system architecture and security best practices
  • Review, digest, and distill complex code and technical topics to ensure clarity and accessibility for all engineers
  • Provide technical leadership, foster collaboration, and drive key initiatives to completion
  • Uphold team values, including engineering excellence, curiosity, bias for action, self-awareness, inclusivity, and openness


  • What You'll Bring

  • Minimum 2+ years of relevant industry experience
  • Experience in developing, scaling, and maintaining infrastructure for distributed systems, including IoT applications
  • Proficiency in many of the following: Linux, Networking, Kubernetes, on-premises data centers, AWS, Terraform, Prometheus, Helm, GitHub Actions, PostgreSQL, and Kafka
  • Strong understanding of system design principles and the challenges of ensuring availability, reliability, scalability, and security in distributed software systems
  • Effective verbal and written communication skills
  • Ability to navigate uncertainty and loosely defined problem statements
  • Strong analytical and problem-solving skills, with the ability to evaluate trade-offs and make well-reasoned decisions
  • Collaborative mindset with a willingness to learn, mentor, and engage in open discussions


  • , Tesla

    総閲覧数

    1

    応募クリック数

    0

    模擬応募者数

    0

    スクラップ

    0

    Teslaについて

    Tesla

    Tesla

    Public

    A financial leasing taxi company that provides vehicles to customers

    140,000+

    従業員数

    Ciudad De Panamá

    本社所在地

    $800B

    企業価値

    レビュー

    3.1

    5件のレビュー

    ワークライフバランス

    1.5

    報酬

    1.2

    企業文化

    1.3

    キャリア

    1.8

    経営陣

    1.1

    15%

    友人に勧める

    良い点

    Strong financial performance

    Revenue growth

    Company achieving targets

    改善点

    Poor compensation and raises below inflation

    Union-busting and anti-labor practices

    Unpaid work demands and wage theft

    給与レンジ

    1,397件のデータ

    Junior/L3

    Mid/L4

    Junior/L3 · Associate Analyst

    2件のレポート

    $94,875

    年収総額

    基本給

    $82,500

    ストック

    -

    ボーナス

    -

    $92,000

    $97,750

    面接体験

    4件の面接

    難易度

    3.5

    / 5

    期間

    14-28週間

    体験

    ポジティブ 0%

    普通 75%

    ネガティブ 25%

    面接プロセス

    1

    Application Review

    2

    Recruiter Screen

    3

    Technical Phone Screen

    4

    Take-home Assignment

    5

    Panel Interview

    6

    Offer

    よくある質問

    Coding/Algorithm

    Technical Knowledge

    Behavioral/STAR

    System Design

    Machine Learning Concepts