Role Description The Inbound team develops highly-scalable solutions to determine item attribute values (e.g., color, material, style) and to verify merchandise authenticity. Senior Applied Scientists own the development and deployment of machine learning solutions and influence the technical direction of their team. They will work closely with tech-leads, Product and Engineering partners in the development of the solution. - Develop and deploy Computer Vision and Machine Learning solutions to solve business problems. - Maintain clean, efficient, and scalable code that meets industry standards. - Analyze large datasets to extract actionable insights and make informed decisions. - Employ state-of-the-art Machine Learning methodologies and frameworks to develop robust and scalable models. - Influence technical direction and take ownership of key components of systems and solutions, ensuring that they meet the needs of the business. - Collaborate with key stakeholders in the development of data-driven solutions and deployable products. - Mentor other team members to help establish team domain expertise. - Contribute to the company's intellectual property and technical leadership through patents and publications at top-tier conferences and journals. Qualifications - 5+ years of industry experience in Computer Vision and applied Machine Learning. - Masters Degree or PhD in CS / ML, statistics, or related field, or 8+ years of industry experience. - 3+ years experience in building, deploying, and managing machine learning and deep learning models in production environments at scale. - Deep understanding of Computer Vision and ML algorithms/techniques (CNNs, transformers, GANs, optimizers, regularization) and experiment design and best practices (A/B testing, training/serving pipelines, feature engineering). - Extensive experience in scientific libraries in Python (numpy, pandas) and Machine Learning tools and frameworks (PyTorch, Tensorflow, Keras, Scikit-Learn). - Strong data engineering skills and experience working with large scale datasets. - Experience with experiment automation frameworks (Ray Tune, W&B, Kubeflow). - Experience with cloud technologies AWS, GCP or Azure. - Fluency in Python. Requirements - PhD preferred (CS, ML, AI, Stats, OR or related field). - Background in applying ML techniques to solve real-world business problems in the retail sector. - Familiarity with MLOps tools and pipelines. - Impact-focused and passionate about delivering high-quality models.

• Development of machine learning models. • Data preparation and preprocessing. • Training, validation and optimization of models. • Deployment of models to production environments. • Performance monitoring and continuous improvement. • Collaboration with Data and business teams.

Description The Role: General Motors is seeking a Staff AI/ML Engineer for the Vehicle Mechatronic Embedded Controls (VMEC) Analytics team. The team delivers production AI/ML solutions for high-impact diagnostics, prognostics, and test-effectiveness use cases. This is a hands-on practitioner role focused on building, shipping, and operating real systems - not on academic research. The Staff AI/ML Engineer will serve as a senior individual contributor within an established AI/ML leadership group, providing deep technical expertise, shaping implementation approaches, and mentoring others while collaborating on overall strategy. What You'll Do: - Design, build, and operate end-to-end AI/ML solutions (data pipelines, models, services, and tools) for diagnostics, prognostics, and test analytics. - Implement production-grade ML pipelines on platforms such as Azure and Databricks, covering data ingestion, feature engineering, training, evaluation, and inference for batch and streaming workloads. - Develop and maintain robust, observable ML services and internal tools that make complex vehicle and field data easy to use for engineers and technical stakeholders. - Apply practical ML and statistical methods (e.g., tree-based models, time-series and anomaly detection, deep learning where appropriate) with a focus on reliability, explainability, and impact. - Own model and data observability in production, including metrics, dashboards, alerts, and remediation workflows for drift, data quality, and performance regressions. - Partner with data engineering to define and use industrialized and vectorized data products that support search, RAG, and analytics at scale. - Review designs and code, mentor AI/ML practitioners, and help set high standards for testing, logging, deployment, and documentation. - Collaborate with diagnostics/prognostics SMEs, validation, safety, and program teams to prioritize work, define success metrics, and embed solutions in day-to-day engineering workflows. Your Skills & Abilities (Required Qualifications) : - Graduate degree (Master's or PhD) in Computer Science, Data Science, Machine Learning, Statistics, Engineering, or a closely related quantitative field. - 7+ years of hands-on experience designing, building, and operating machine learning systems in production environments. - Strong proficiency in Python (production-quality code, testing, packaging) and SQL, with experience working in shared, multi-developer codebases. - Practical experience with core ML frameworks such as PyTorch, TensorFlow, or scikit-learn, and with MLOps tooling (e.g., MLflow, CI/CD, model registries, experiment tracking). - Experience building data and ML workloads on cloud platforms, preferably Microsoft Azure, and working with Databricks, Spark, or similar distributed processing frameworks. - Demonstrated ability to turn ambiguous real-world problems into shippable AI/ML solutions, owning the details from data exploration through deployed service and ongoing operation. - Strong understanding of ML system behavior in production (data issues, non-stationarity, latency, throughput, failure modes) and comfort debugging with logs, metrics, and traces. - Excellent communication and collaboration skills, with a track record of influencing decisions and mentoring other AI/ML practitioners. What Will Give You A Competitive Edge (Preferred Skills) : - 10+ years of applied machine learning or data science experience, including ownership of high-impact, production AI systems. - Experience with vehicle, fleet, or telematics data, or adjacent domains with rich time-series and reliability data. - Background in diagnostics/prognostics modeling (e.g., fault classification, anomaly detection, degradation modeling, survival analysis). - Experience building vector search and retrieval-augmented generation (RAG) or similar production AI applications that integrate foundation models with structured data. - Familiarity with Azure Cognitive Services or similar managed AI services and how to combine them pragmatically with custom ML for robust production solutions. - Demonstrated impact in raising engineering standards and building AI/ML engineering capability across teams. - Prior experience in automotive, embedded controls, or software-defined vehicle programs, or other safety-critical domains. GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc.) This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate. About GM Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all. Why Join Us We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team. Total Rewards | Benefits Overview From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources. Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire. Accommodations General Motors offers opportunities to all job seekers including individuals with disabilities. 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• Extend, optimize, and maintain core data models that support customer-facing reports, machine learning, and generative AI workloads. • Build and operationalize ML and AI workflows that streamline operations, reduce manual effort, and improve customer and business outcomes. • Partner with engineering, product, and analytics teams to deliver seamless integrations and customer-facing data products. • Implement data quality, observability, and governance frameworks to ensure reliable, well-managed data at scale. • Document data flows, integration contracts, and operational runbooks to support efficient scaling and handoff.