Senior Research Scientist, Machine Learning

As a Senior Research Scientist at Syntiant Corp, you'll be tasked with developing cutting-edge audio and vision machine learning models primarily for edge devices. Your key responsibilities will include optimizing and implementing prototypes to validate new approaches efficiently, training models for high accuracy, and collaborating with software teams for seamless integration. You’ll also stay informed about the latest research to ensure that your solutions remain at the forefront of the industry.

To qualify for the Senior Research Scientist role at Syntiant Corp, candidates should possess at least an MS or PhD in Computer Science or a related field, along with a minimum of 5 years of industrial experience in developing ML models specifically for audio or vision applications. Expertise in programming languages like C++ and Python is essential, as is a solid understanding of deep learning architectures and frameworks such as TensorFlow or PyTorch.

Success as a Senior Research Scientist in Machine Learning at Syntiant requires a combination of technical skills and innovative thinking. Candidates should have strong programming capabilities, proficient in C++ and Python, along with experience using ML frameworks. A solid grasp of deep learning architectures, analytical problem-solving skills, and the ability to rapidly iterate on complex solutions will also be key in achieving goals within this role.

Syntiant Corp fosters a collaborative, innovative environment that encourages the professional growth of its team members, including Senior Research Scientists in Machine Learning. You'll have access to the latest research, opportunities to prototype and test new ideas quickly, and the chance to work alongside some of the industry's top talent. Continuous learning and adapting to new technologies are integral to our culture, helping you grow your skills and career trajectory.

The salary range for the Senior Research Scientist, Machine Learning position at Syntiant Corp is targeted between $180,000 and $230,000. Compensation is determined based on various factors, including individual experience, skill sets, and market conditions. Syntiant values the diverse backgrounds of candidates and adjusts offers accordingly to ensure equitable and competitive pay.

Supervised learning involves training a model on labeled data, where the algorithm learns to map input data to the correct output based on examples. Conversely, unsupervised learning deals with unlabeled data, allowing the model to identify patterns or groupings on its own. Be prepared to provide examples of projects you've worked on that utilized either technique.

To optimize machine learning models, I leverage techniques such as hyperparameter tuning, regularization to prevent overfitting, and employing cross-validation methods to ensure model robustness. I also focus on feature selection to improve accuracy and efficiency. Discussing specific instances where these techniques improved model performance can showcase your practical expertise.

Dealing with imbalanced datasets can include techniques like oversampling the minority class, undersampling the majority class, or using algorithms that are robust against imbalances, such as tree-based models. I also emphasize the importance of performance metrics that reflect the model's capability across classes, such as AUC-ROC or precision-recall curves.

My experience with deep learning frameworks includes developing and training models using TensorFlow and PyTorch. I've utilized TensorFlow for large-scale production models and PyTorch for research-focused prototypes due to its flexibility. Be ready to detail specific projects where you've implemented these frameworks effectively.

During a project involving real-time audio recognition, I faced challenges with noise interference. I addressed this by implementing data augmentation techniques and utilizing a convolutional neural network (CNN) architecture optimized for audio processing. Sharing the specifics of your problem-solving process and the outcomes will demonstrate your analytical skills.

To maintain model relevance and accuracy, I advocate for continuous monitoring of model performance and implementing a feedback loop to retrain the models with new data. Regularly revisiting the model’s assumptions and retraining it as necessary ensures it effectively adapts to new patterns in the data.

Model interpretability is crucial, especially in applications with significant consequences. I approach it using techniques like LIME or SHAP, which help explain model predictions and highlight influential features. Discussing how you've improved interpretability in your previous work can reflect your dedication to responsible AI.

For audio applications, I commonly use methods like Mel-frequency cepstral coefficients (MFCC) and spectrograms as feature representations. In vision applications, techniques such as CNNs for feature extraction help capture spatial hierarchies in images. Sharing your experience with these techniques could illustrate your deep understanding of the field.

I typically evaluate models using a combination of performance metrics that align with business objectives and cross-validation methods to ensure robustness. Metrics like precision, recall, and F1 score are essential in gauging efficacy in imbalanced scenarios. Be prepared to provide examples of validation techniques you've effectively utilized.

My approach involves collaborating closely with software engineering teams to ensure seamless integration of ML models. This involves understanding the architecture of existing systems and using APIs or additional layers to ensure efficient model deployment. Discuss past experiences where you successfully implemented models into production environments.