Machine Learning Engineer

Talentify; Fremont (CA); On-site; USD 130,000 - 170,000; Full time

Job summary

Tesla is seeking a Machine Learning Engineer in Fremont, CA, onsite. The role involves translating complex business problems into ML solutions, building end-to-end ML pipelines, and handling multimodal data.

You will collaborate with cross-functional teams to deploy and monitor models in production, optimizing for performance and scalability. Strong foundations in ML theory and hands-on with PyTorch/TensorFlow are required.

Qualifications

• Proficiency in Python and C++ for ML development and integration.

Responsibilities

• Translate ambiguous problems into ML solutions.
• Build end-to-end ML pipelines using PyTorch/TensorFlow and Pandas.
• Handle multimodal data including images, sensor outputs, voice, text, and tabular data.
• Collaborate with production, process, controls, and quality teams to integrate ML solutions.
• Deploy, monitor, and maintain models with alerting systems.

Skills

Python

C++

ML theory

Model deployment

Data preprocessing

Data analysis

Tools

PyTorch

TensorFlow

Pandas

TensorRT

Job description

Position : Machine Learning Engineer

Experience : 9+yrs

Visa : GC, USC, GCEAD, H4EAD, TN

Tax Term : W2

Client : Tesla

Location : Fremont, CA, onsite

Project Description

Design, develop and implement critical machine learning models that operate on our factory and warehouse environments

Duties/Day to Day Overview

1. Translating Ambiguous Problems into ML Solutions

You will take loosely defined or complex business and operational problems and determine how to solve them using machine learning. This involves clarifying requirements, designing an approach, and selecting the right algorithms and architectures (e.g., supervised learning, CNNs).

2. Building End-to-End Machine Learning Pipelines

You will design, implement, and train ML models using frameworks like PyTorch and TensorFlow, leveraging data tools like Pandas for preprocessing and analysis. The process will include:

• Data gathering
• Cleaning and preprocessing
• Model training and evaluation
• Optimization for performance and efficiency
• Deployment to production environments

3. Handling Complex, Multimodal Data

You will work with large and varied datasets — including images, multi-spectral sensor outputs, voice, text, and tabular data — and develop preprocessing strategies to make this data usable for machine learning models.

4. Collaborating with Cross-Functional Teams

You will partner with production, process, controls, and quality teams to understand operational pain points and design ML-based solutions that integrate seamlessly into existing workflows and systems.

5. Deploying, Monitoring, and Maintaining Models

You will own models after deployment, setting up robust alerting and monitoring systems to track performance, detect issues, and initiate quick fixes when needed.

6. Optimizing Algorithms for Performance

You will improve speed and efficiency through quantization, pruning, and TensorRT conversion, ensuring that models meet performance requirements in real-world environments — including embedded or firmware-integrated contexts (leveraging C++ if needed).

7. Applying Strong Theoretical Foundations

You will use expertise in linear algebra, geometry, probability theory, numerical optimization, and statistics to design models, assess feasibility, and ensure rigorous evaluation.

8. Specializing in High-Impact Domains

Depending on the project, you may work on problems in computer vision, large language models, recommender systems, or operations research, applying domain-specific techniques to deliver maximum value.

9. Writing High-Quality, Sustainable Code

You will produce clean, modular, and maintainable code to ensure that ML solutions are scalable and easy to update, supporting long-term sustainability of deployed systems.

Top Requirements

(Must haves)

Algorithm Development & Optimization

• Rapid prototyping of algorithms for high-performance, data-intensive applications.
• Optimization for speed, efficiency, and scalability in production environments.

2. Programming & Integration

• Python – advanced expertise for data processing, ML model development, and automation.
• C++ – desirable proficiency for integration with vehicle firmware and full product lifecycle delivery.

3. Mathematical & Statistical Foundations

• Strong background in:
• Linear Algebra and Geometry – essential for ML, graphics, and computer vision.
• Probability Theory – for modeling uncertainty and decision-making.
• Numerical Optimization – for training and refining models.
• Statistics – for model evaluation and performance analysis.

4. Deep Learning Frameworks

• Hands-on experience with PyTorch and TensorFlow for model development and deployment.

5. Model Optimization & Deployment

• Skilled in performance-enhancing techniques:
• Quantization
• Pruning
• TensorRT conversion
• Deploying and maintaining production machine learning use cases.

6. Domain Expertise

• Proficiency in at least one specialized area:
• Computer Vision
• Large Language Models (LLMs)
• Recommender Systems
• Operations Research

7. Software Engineering Best Practices

• Writing clean, sustainable, and modular code.
• Translating research prototypes into robust, production-ready systems.