Staff TLM - Scalable Machine Learning job opportunity at Latitude AI.

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Latitude AI Staff TLM - Scalable Machine Learning
Experience: 7-years
Pattern: full-time
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Intelligent Systems

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Hiring inbound within Palo Alto, CA, Detroit, MI, Pittsburgh, PA,

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.   When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people.  As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif. Meet the team: In the Intelligent Systems team, we convert photons into understanding, primarily via computer vision and machine learning. Our organization is responsible for all downstream tasks of the vehicle sensor suite onboard, as well as the full offboard/cloud infrastructure for machine learning at Latitude. What you’ll do:  Scaling our machine learning models with data is vital to achieve optimal performance and to enable a safer and more sustainable future of transportation. In the Scalable ML team, we are looking at methods to improve the efficiency of model training, enabling ML developers to work efficiently with large volumes of data. In your role as the manager for the Scalable ML team, you will collaborate with perception and software experts to build solutions that scale. Lead and mentor a team of software engineers, driving high technical standards and continuous skill development for scalable machine learning infrastructure Provide technical leadership and architectural vision for the design and implementation of solutions to generate, manage, and process large amounts of multi-sensor data for machine learning Architect and guide the development of cloud services for data ingest, workflow orchestration, and distributed training at scale Drive the implementation of advanced MLOps practices, including model artifact management, experiment tracking, and automated model deployment and testing strategies Collaborate with cross-functional teams to integrate ML pipelines, ensuring the delivery of high-quality, production-ready software with a strong focus on reliability and stability What you'll need to succeed: Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 7+ years of relevant experience (or Master's degree and 5+ years of relevant experience, or PhD and 2+ years of relevant experience) Minimum of 7 years of experience in software development, with at least 3 years in a technical leadership or senior architect role Extensive experience designing, building, and operating large-scale distributed systems and cloud infrastructure for machine learning Proven expertise in developing and optimizing machine learning data pipelines, including data generation, management, and distributed training Strong proficiency in python for developing high-performance, production-quality software Demonstrated ability to architect complex software solutions for machine learning and autonomous systems Track record of successfully delivering complex software projects from conception to production, emphasizing scalability and reliability Nice to have:  Master's or PhD in Computer Science, Electrical Engineering, Robotics, or a closely related field Hands-on experience with distributed machine learning frameworks and tools such as Ray, PyTorch, Dagster, LakeFS, or PyArrow Experience with multi-sensor data fusion and processing techniques relevant to autonomous driving applications Familiarity with safety-critical software development principles in automotive or robotics domains Deep understanding of machine learning concepts, computer vision use cases, and the challenges of L2/L3 autonomy What we offer you: Competitive compensation packages High-quality individual and family medical, dental, and vision insurance Health savings account with available employer match Employer-matched 401(k) retirement plan with immediate vesting Employer-paid group term life insurance and the option to elect voluntary life insurance Paid parental leave Paid medical leave Unlimited vacation 15 paid holidays Daily lunches, snacks, and beverages available in all office locations Pre-tax spending accounts for healthcare and dependent care expenses Pre-tax commuter benefits Monthly wellness stipend Adoption/Surrogacy support program Backup child and elder care program Professional development reimbursement Employee assistance program Discounted programs that include legal services, identity theft protection, pet insurance, and more Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives Learn more about Latitude’s team, mission and career opportunities at lat.ai! The expected base salary range for this full-time position in California is $218,240 - $327,360 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements. Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position. We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

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