Sr. Machine Learning Engineer

At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have an exciting opportunity for you to join our expanding area of Prognostics. 

Are you enthusiastic to mine raw data and realize its hidden value by building amazing, connected data solutions that benefit our customers? Would you love to accelerate our efforts in implementing advanced physics and ML Models in production?

The Sr. Machine Learning Engineer role resides within the Ford’s Electric Vehicle organization. In this role, you will work on building scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support the Ford vehicle prognostic initiatives. 

What you will do...

• Develop and optimize ML algorithms working with data scientists and data engineers to deploy to production.
• Use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy.
• Train and re-train ML models and systems as required.
• Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios.
• Automate model deployment, training, and re-training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps.
• Work closely with cloud architect, product manager, product owner and developers to build and enhance MLOPs platform in GCP.
• Lead by example in use of Paired Programming for cross training/upskilling, problem solving, and speed to delivery.
• Fulfill problem formulation and ML technique consulting requests in a timely manner.
• Enable model management for model versioning and traceability.
• Continuously optimize and enhance existing ML data solutions (pipelines, products, infrastructure) for best performance, high security, low vulnerability, low costs, and high reliability.
• Demonstrate technical knowledge and communication skills with the ability to advocate for well-designed solutions.
• Continuously enhance your domain knowledge of connected vehicle data, connected services and algorithms/models developed by data scientists within Ford.
• Stay current on the latest ML engineering practices and contribute to the technical direction of the company while keeping a customer-centric approach.
• Help innovate and standardize machine learning development practices.

You will have…

• Master’s degree or foreign equivalent degree in Computer Science, Software Engineering, Information System, Data Engineering, or a related field.
• 5 years of professional experience in:
  • Data engineering, data product development and software product launches
  • At least three of the following languages: Java, Python, Spark, Scala, SQL
  • Developing Machine Learning algorithms working with Data Scientists, optimizing them, and deploying them to production.
  • Cloud data/software engineering experience building scalable, reliable, and cost-effective production batch and streaming data pipelines.

Even better if you have…

• Ph.D. or foreign equivalent degree in Computer Science, Software Engineering, Information System, Data Engineering, or a related field.
• 3 years of experience mentoring engineers and leading large projects.
• Demonstrated contributions and expertise in two or more of the following domains:
  • Regression algo for interpolation and extrapolation (both linear / non-linear)
  • Time series analysis and statistics, autoregressive models, filtering algorithms
  • Gaussian processes and/or kernel methods, Bayesian statistics
  • Modeling with different neural network architectures: MLP, CNN, RNN
  • Quantitative model performance assessment using cross-validation, blind testing
  • Physics-informed neural networks (ML for computational fluid dynamics or finite element analysis, point cloud or mesh-based neural networks, PDE surrogate modelling)
  • Uncertainty quantification and propagation for time series analysis and forecasting.
• Demonstrated ability to document complex systems.
• Demonstrated commitment to quality and project timing.
• Committed code to improve open-source data/software engineering projects.
• Passion to experiment/implement state of the art ML engineering methods/techniques.
• Experience working in an implementation team from concept to operations, providing deep technical subjec