Perfict Global
Overview
Position :
Machine Learning Engineer
Hit Apply below to send your application for consideration Ensure that your CV is up to date, and that you have read the job specs first. 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.
#J-18808-Ljbffr
Position :
Machine Learning Engineer
Hit Apply below to send your application for consideration Ensure that your CV is up to date, and that you have read the job specs first. 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.
#J-18808-Ljbffr