AI/ML Scientist

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

This is a Hybrid position within our Engineering organization. The role will allow employees to work remotely, but will also require onsite work based on business needs. The selected candidate will be expected to live a commutable distance to the technical or innovation center they are selected for. Relocation will be provided as needed.

About GM

We’re dedicated to achieving our vision of a world with Zero Crashes, Zero Emissions and Zero Congestion. We are looking for people who are passionate about helping us create safer, better and more sustainable solutions for personal mobility. Our bold vision won’t happen overnight, but just as we transformed how the world moved in the last century, we are committed to transforming how we move today and in the future.

Why Work for Us

Our culture is focused on building inclusive teams, where differences and unique perspectives are embraced so you can contribute to your fullest potential as you pursue your career. Our locations feature a variety of work environments, including open work spaces and virtual connection platforms to inspire productivity and flexible collaboration. And we are proud to support our employees volunteer interests, and make it a priority to join together in efforts that give back to our communities.

This is a unique opportunity to translate subject matter expertise and experience in practices around virtuous cycles for machine learning data as part of a multi-disciplinary team of experienced individuals. This team will drive the solution design, development, and deployment of advanced statistical and mathematical solutions using machine learning techniques as well as traditional methods. The team will generate new algorithmic and software models enabling enterprise tools for automated and autonomous driving systems development.

• Candidates should have deep familiarity with predictive modeling techniques based off time series analysis with very high dimensional data sets, especially regarding kinematics.
• Experience in generating geospatial feature detection models such as land use, land type, or object detection leveraging sensor fusion or imagery classification is highly desired.
• Experience establishing sampling policies for understanding real-world dynamic events from small samples of data, and methodological bases for calculating comprehensive statistics needed to validate expected performance of models or generalization from small samples.
• Experience translating real-world sensor data into simulated environments and vice-versa.
• Familiarity with linear discriminant analysis, cluster analysis, and other methods to identify latent class features in both labeled and label-free data.

Additional Description

Qualifications:

The candidate will support iterative development and learning cycles in the areas listed below, and ultimately produce new and creative solutions that will become part of the operating fabric of the global enterprise. The scope of the work may vary and the candidate should demonstrate proficiency in one or more of the following:

• Geospatial analysis and mapping
• Mobility and behavioral analysis
• Risk analysis and forecasting
• Comparative frameworks between sparse real-world observations, dense simulated observations, and opportunities to expand sensor or sensing frameworks
• Emerging techniques such as generative adversarial networks, self-supervised reinforcement learning, and imitation learning for complex optimization problems
• Numerical optimization, statistical modeling and forecasting, machine learning and image processing

Skills:

Candidates must be collaborative team players who will work closely with fellow Engineering professionals (scientists, engineers, software developers, and team leads). Further, candidates should be able to demonstrate the following capabilities:

• Working on small, high output teams in a fast-paced environment
• Excellent verbal and written communication skills with the ability to interact effectively with multiple stakeholders
• Strong problem-solving skills and analytical thinking
• Excellent attention to detail
• Ability to deep dive into data or related analyses/reports and derive useful insights to support decision making

Responsibilities:

• Provide technical guidance and subject matter expertise to team members and stakeholders
• Build predictive models and machine-learning algorithms
• Analyze large amounts of information to discover trends and patterns
• Undertake preprocessing of structured and unstructured data
• Monitor and sustain model effectiveness
• Combine models through ensemble modeling
• Present complex information using data visualization techniques
• Propose solutions and strategies to business challenges that drive business impact

Requirements:

• MS Degree (PhD Preferred) in Engineering, Computer Science, Physics, Mathematics, or related quantitative field
• 5 or more years proficiency in one or more core analytical tools / suites / languages such as Python, R, Spark Scala, PyTorch, TensorFlow, and understand their limitations
• Preferred experience with autonomous vehicle and/or ADAS technologies/systems and related vehicle data processing and analytics

The candidate should have experience (3+ years outside of a graduate school assistantship) across a broad set of potential roles, including:

• Research, Data, or AI/ML Scientist
• AI Analyst
• Engineer
• AI/ML Developer or Architect