Senior Staff Engineer - Machine Learning

Publiceringsdatum: 2025-12-18

Letar du efter ett jobb inom data/it? En möjlighet har öppnat upp för dig att börja arbeta som civilingenjör, systemutveckling hos Qualcomm Auto Ltd Sweden Filial. Detta är ett jobb på heltid där Qualcomm Auto Ltd Sweden Filial letar efter någon under tills vidare.

För detta jobb är Stockholm den angivna tjänsteorten. Qualcomm Auto Ltd Sweden Filial behöver fler anställda som stödjer arbetet inom civilingenjör, systemutveckling. Kolla in deras lediga tjänst för att se om du har vad som krävs. Bli en del av Qualcomm Auto Ltd Sweden Filial genom att ansöka till jobbet som civilingenjör, systemutveckling senast söndag den tjugoåttonde december 2025.

Jobbannons

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Machine Learning Engineer, you will create and implement machine learning techniques, frameworks, and tools that enable the efficient discovery and utilization of state-of-the-art machine learning solutions over a broad set of technology verticals or designs. Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software.

Preferred Qualifications:

Master's degree in computer science, Engineering, Information Systems, or related field.

5+ years of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras).

5+ years of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware.

5+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media).

5+ years of experience with one or more programming language suitable for machine learning (e.g., Python, R, C, C++)

5+ years of experience using statistics and probability (e.g., conditional probability, Bayes rule).

3+ years of experience working in a large matrixed organization.

2+ years of work experience in a role requiring interaction with senior leadership (e.g., Director and above).

1+ year in a technical leadership role with or without direct reports (only applies to positions with direct reports).

Developed 1+ novel Machine Learning architecture(s).

Principal Duties and Responsibilities:

Leverages advanced Machine Learning knowledge to extend training or runtime frameworks or model efficiency software tools with new features and optimizations.

Models, architects, and develops highly advanced machine learning hardware (co-designed with machine learning software) for inference or training solutions.

Develops critical optimized software to enable AI models deployed on hardware (e.g., machine learning kernels, compiler tools, or model efficiency tools, etc.) to allow specific hardware features; collaborates with hardware teams for joint design and development.

Leads the development and application of machine learning techniques into products and/or AI solutions to enable customers to do the same.

Develops, adapts, or prototypes novel machine learning solutions aligned with and motivated by proposals or roadmaps for complex products and system-level features.

Oversees and provides technical expertise to others conducting experiments to train and evaluate machine learning solutions.

Level of Responsibility:

Works independently with minimal supervision.

Provides supervision/guidance to other team members.

Decision-making is significant in nature and affects work beyond immediate work group.

Requires verbal and written communication skills to convey complex information. May require negotiation, influence, tact, etc.

Has a moderate amount of influence over key organizational decisions (e.g., is consulted by senior leadership to make key decisions).

Tasks do not have defined steps; planning, problem-solving, and prioritization must occur to complete the tasks effectively.