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Tesla Machine Learning Engineer Jobs (NOW HIRING)

Forhyre

Machine Learning Engineer

New York, NY

We are looking for a Machine Learning Engineer to help us create artificial intelligence products. Machine Learning Engineer responsibilities include creating machine learning models and retraining ...

Paylocity

Machine Learning Engineer

Machine Learning Engineer Position Overview Paylocity is growing its Machine Learning Engineering organization! Our machine learning engineering team is responsible for developing infrastructure and ...

Paylocity

Machine Learning Engineer

Schaumburg, IL · On-site

Machine Learning Engineer Position Overview Paylocity is growing its Machine Learning Engineering organization! Our machine learning engineering team is responsible for developing infrastructure and ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

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Tesla Machine Learning Engineer information

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$31.5K

$128.8K

$193.5K

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

As of Jul 30, 2026, the average yearly pay for tesla machine learning engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Tesla Machine Learning Engineer position, and why are they important?

To thrive as a Tesla Machine Learning Engineer, you need a solid background in computer science, mathematics, and machine learning, often supported by a relevant degree and practical experience with AI models. Proficiency with programming languages like Python or C++, deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud-based computing platforms are typically required. Strong problem-solving abilities, collaboration, and effective communication set top candidates apart in a team-oriented, innovative environment. These skills and qualities are crucial for developing robust AI solutions that drive Tesla's cutting-edge technologies and support its mission of advancing sustainable transport.

What is a Tesla Machine Learning Engineer job?

A Tesla Machine Learning Engineer develops and optimizes AI models for applications such as autonomous driving, manufacturing automation, and energy management. They work with large datasets, train deep learning models, and deploy solutions to improve Tesla’s technologies. The role involves collaboration with software engineers, data scientists, and hardware teams to enhance performance and efficiency. Strong programming skills, proficiency in frameworks like TensorFlow or PyTorch, and experience with real-world machine learning deployment are essential.

What are the most common challenges faced by Tesla Machine Learning Engineers, and how are they addressed?

Tesla Machine Learning Engineers often tackle challenges such as processing large-scale data sets, optimizing models for real-time performance, and accommodating frequent changes in project requirements. These challenges are addressed by leveraging Tesla’s high-performance computing resources, working closely with cross-functional teams—including hardware, software, and data engineering—and continuously iterating on solutions. The collaborative culture at Tesla encourages knowledge sharing and innovative problem-solving, helping engineers adapt quickly. If you're motivated by complex challenges and rapid innovation, you'll find opportunities to learn and grow while making direct impacts on disruptive products.

More about Tesla Machine Learning Engineer jobs
What cities are hiring for Tesla Machine Learning Engineer jobs? Cities with the most Tesla Machine Learning Engineer job openings:
What are the most commonly searched types of Tesla Machine Learning Engineer jobs? The most popular types of Tesla Machine Learning Engineer jobs are:
What job categories do people searching Tesla Machine Learning Engineer jobs look for? The top searched job categories for Tesla Machine Learning Engineer jobs are:
Tesla Machine Learning Engineer jobs near you
Infographic showing various Tesla Machine Learning Engineer job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer, Model Quantization, Tesla AI

Tesla

Palo Alto, CA • On-site

Full-time

Re-posted 10 days ago

Tesla rating

8.5

Company rating: 8.5 out of 10

Based on 679 frontline employees who took The Breakroom Quiz

1st of 44 rated automakers

Job description

Job Summary:
Tesla is a leading company in the field of AI, focusing on building foundational models for real-world autonomy. The Machine Learning Engineer will be responsible for architecting and scaling quantization pipelines for multi-modal foundation models, optimizing inference latency and power consumption for various applications including self-driving cars and robots.
Responsibilities:
• Architect and scale quantization pipelines (both Post-Training Quantization and Quantization-Aware Training) for massive multi-modal foundation models that fuse vision, prediction, and decision-making. You will optimize inference latency, memory bandwidth utilization, and power consumption for self-driving cars, Optimus robots, and digital agents operating at enterprise scale
• Innovate quantization-aware-training recipes and algorithms that tackle complex optimization challenges inherent to low-precision training
• Push the limits of low-precision AI: Research and implement advanced low-bitweight post-training quantization techniques to address hard algorithmic problems such as activation outlier mitigation, KV cache compression, and optimal layer-wise bit-allocation while strictly maintaining model accuracy
• Collaborate closely with AI compiler, inference engine, and silicon teams to ensure models are architected to maximally utilize underlying hardware capabilities by co-designing quantization-friendly architectures, hardware-aware sparsity patterns, and mixed low-precision kernels
• Collaborate across perception, planning, robotics, digital agents, and infrastructure teams to move models from research to fleet-wide, robot-wide, and enterprise-wide deployment
Qualifications:
Required:
• Degree or equivalent experience in Computer Science, Machine Learning, Robotics, Computer Vision, or related quantitative field
• 2+ years of hands-on experience training, optimizing, and deploying large-scale quantized deep learning models
• Strong technical understanding of the challenges inherent to quantizing large transformer architectures, including mitigating massive activation outliers, KV cache quantization, and maintaining the numerical stability of attention mechanisms at low precision
• Deep expertise in the theory and low-level implementation of modern quantization algorithms (e.g., GPTQ, AWQ, SmoothQuant, OmniQuant)
• Experience with low-level numerics and emerging data formats (e.g., FP8, INT4, W4A8, W8A8, micro-scaling/MX formats) and their trade-offs regarding latency, memory bandwidth, and model fidelity
• Rigorous understanding of computer architecture and the roofline model. Familiarity with how to optimize for memory hierarchies, minimize SRAM/DRAM data movement, and efficiently map quantized GEMMs and memory-bound operators to custom silicon
• Proficiency in writing custom CUDA/Triton kernels, implementing custom autograd functions (e.g., Straight-Through Estimators), and manipulating PyTorch computational graphs (e.g., FX tracing, torch.compile)
• Strong software engineering skills — clean, production-grade Python/C++ code that ships reliably at scale
• Proven ability to turn cutting-edge research into robust, real-world systems that improve safety, capability, efficiency, or digital productivity
• Passion for Tesla’s mission and excitement about deploying AI that moves both the physical and digital worlds forward
Company:
Tesla is an electric vehicle and clean energy company that provides electric cars, solar, and renewable energy solutions. Founded in 2003, the company is headquartered in Austin, USA, with a team of 10001+ employees. The company is currently Late Stage.

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