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Senior Tesla Machine Learning Engineer Jobs in Santa Rosa, CA

Nextdoor

Machine Learning Engineer - Ads

Bodega Bay, CA

$205K - $355K/yr

Finally, you will help build the foundational patterns that ML engineers will use for years to come as we ramp up our effort to introduce machine learning into our platform * Collect and gather ...

Apple

HomeKit Machine Learning Engineer

Bodega Bay, CA · On-site

$129K - $194K/yr

As a Machine Learning Engineer you will have an opportunity to be part of Smart Home focused ML innovation within Apple, initially focused on Applied ML for Computer Vision. The team is well ...

Ema

Software Engineer, Machine Learning

Bodega Bay, CA · On-site

$160K - $220K/yr

Who You Are We're looking for innovative and passionate Machine Learning Engineers to join our team. You are someone who loves solving complex problems, enjoys the challenges of working with huge ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA · On-site

$142K - $263K/yr

We are committed to pushing the boundaries of innovation and engineering excellence in product designs through machine learning and FEA simulations. We truly believe in the power of predictive ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA · On-site

$142K - $263K/yr

We are committed to pushing the boundaries of innovation and engineering excellence in product designs through machine learning and FEA simulations. We truly believe in the power of predictive ...

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All JobsTesla Machine Learning Engineer JobsSenior Tesla Machine Learning Engineer JobsSenior Tesla Machine Learning Engineer Jobs in Santa Rosa, CA

Senior Tesla Machine Learning Engineer information

See Santa Rosa, CA salary details

$65.1K

$138.4K

$200.6K

How much do senior tesla machine learning engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for senior tesla machine learning engineer in Santa Rosa, CA is $138,369.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,300.00 and $156,900.00 per year, depending on experience, location, and employer.

What does a senior Tesla machine learning engineer do?

A Senior Tesla Machine Learning Engineer leads the development and deployment of advanced machine learning models to improve Tesla’s products, such as Autopilot, Full Self-Driving, and manufacturing optimization. They collaborate with multidisciplinary teams to collect data, design algorithms, and ensure models are robust and scalable. In this role, engineers are expected to mentor junior staff, drive research initiatives, and help translate cutting-edge AI advancements into real-world Tesla applications.

What are the key skills and qualifications needed to thrive as a senior Tesla machine learning engineer?

To thrive as a Senior Tesla Machine Learning Engineer, you need deep expertise in machine learning algorithms, strong programming skills in Python or C++, and a proven track record in deploying models at scale, often supported by an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow or PyTorch, experience working with large datasets, and cloud computing platforms are typically required, as well as knowledge of Tesla's proprietary systems. Exceptional problem-solving, collaboration, and communication skills distinguish top performers in this role. These abilities are crucial for developing advanced AI solutions that power Tesla's autonomous systems and for driving innovation in a highly competitive, fast-evolving environment.

How does a senior Tesla machine learning engineer typically collaborate with cross-functional teams?

As a Senior Machine Learning Engineer at Tesla, you will frequently work alongside software developers, data scientists, product managers, and hardware engineers. Collaboration is highly cross-functional, with regular meetings to align on project goals, data requirements, and model deployment strategies. You may be involved in translating business objectives into machine learning solutions, sharing insights with non-technical stakeholders, and refining algorithms based on feedback from various departments. This collaborative environment fosters innovation and ensures that machine learning models are well-integrated into Tesla's products and systems.

What is the difference between Senior Tesla Machine Learning Engineer vs Data Scientist?

AspectSenior Tesla Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, EE, or related; experience in ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops ML models for autonomous vehicles, energy, and manufacturingAnalyzes data to extract insights, supports product and business decisions
Employer & Industry UsageTesla, automotive, energy, AI projectsVarious industries including tech, finance, healthcare

While both roles involve working with data and algorithms, the Senior Tesla Machine Learning Engineer focuses on developing and deploying machine learning models for Tesla's products, especially autonomous systems. In contrast, a Data Scientist primarily analyzes data to inform business decisions across various industries. The ML Engineer role requires deeper expertise in machine learning frameworks and deployment, whereas Data Scientists focus more on statistical analysis and data visualization.

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Senior Tesla Machine Learning Engineer jobs near you

Machine Learning Engineer

Kanak Elite Services Inc

Bodega Bay, CA • Remote

Contractor

Re-posted 21 days ago

Job description

Hello There,

My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of Machine Learning Engineer with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .

Job Description

Title:  Machine Learning Engineer
Location:  South San Francisco, CA  - hybrid role in Bay Arear
Position Type:  Contract 
 

Note: DO NOT SEND WITHOUT MOLECULAR EXPERIENCE, 

Work on ML workflows for molecular property prediction & generative modeling to accelerate drug discovery. 3–5 yrs esp. or PhD with publications in molecular design.

Must have Masters or PH.D. Must have experience in working environment or while getting Master’s or no to very little work exp with PH.D  in Molecular design. Need to have portfolio of their work or be published. Find me Machine Learning with Molecular experience in Bay Area or someone who will relocate as last resort. 
MindSource is looking for a Machine Learning Engineer to join our client's team in South San Francisco, CA.  They will be developing and deploying advanced computational methods for molecular design.  This is a 12-month hybrid contract.  

About the Role

  • Build pipelines for probabilistic molecular property prediction and Bayesian acquisition to power active learning–driven drug discovery.
  • Engineer workflows for molecular generative modeling and other innovative design approaches.
  • Collaborate with machine learning scientists, engineers, computational chemists, and biologists.
  • Partner with therapeutic development teams to analyze existing molecules and design new candidates.
  • Contribute to ongoing initiatives while driving new research directions.

Qualifications

  • PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or related quantitative field — OR MS + 3+ years of relevant industry experience.
  • Demonstrated expertise in production-ready ML workflows (e.g., PyTorch + Lightning + Weights & Biases).
  • Strong track record of achievement (e.g., high-impact first-author publication or equivalent).
  • Excellent written, visual, and verbal communication skills.

Preferred Experience

  • Knowledge of physical modeling (e.g., molecular dynamics) and cheminformatics (e.g., RDKit).
  • Background in molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, or statistical methods.
  • Hands-on experience with Python, PyTorch, Torch Geometric, PyTorch Lightning, RDKit, and BoTorch.
  • Public portfolio of computational projects (e.g., GitHub).