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Applied ML Engineer
Job Summary
We are looking for a versatile Applied ML Engineer to drive machine learning and perception tasks for edge-intelligent systems in challenging environments. You will handle everything from computer vision and sensor fusion models to lightweight inference pipelines and production fine-tuning. This role offers variety across AI subfields, working with cross-functional teams to deliver robust, efficient solutions.
Essential Functions
- Design, train, and evaluate models for object detection, classification, anomaly detection, and sensor-based inference.
- Optimize model architectures and inference pipelines for embedded and edge hardware with compute and bandwidth constraints.
- Contribute to dataset development, including labeling strategies, data augmentation, synthetic data generation, and domain adaptation.
- Prototype and experiment across computer vision, signal processing, and multi-modal fusion.
- Implement real-time pipelines for sensor data processing on-device and in the cloud.
- Develop tools and scripts for benchmarking, data visualization, and debugging ML performance.
- Stay current with ML research and evaluate applicability to product needs.
Required Qualifications
- 4-10 years of experience in applied machine learning, building and deploying production models.
- Experience shipping production ML models in domains like computer vision, signal processing, anomaly detection, or sensor data.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Held a staff or senior lead role with architectural ownership.
- Proven track record at a startup or newer tech company.
- Must be eligible to obtain and maintain a security clearance.
Preferred Qualifications
- Master’s or PhD in Computer Vision, Machine Learning, Robotics, or related field; Bachelor’s considered case-by-case.
- Experience with edge or embedded ML deployments, including model compression and hardware-aware optimization.
- Familiarity with diverse data types like images, time-series, geospatial, and RF.
- Background in regulated industries, preferably government or defense.
- History of academic publications in relevant ML fields.
Technical Skills
- Python
- PyTorch
- TensorFlow
- Computer vision
- Sensor fusion
- Object detection
- Anomaly detection
- Signal processing
- Multi-modal models
- ML Ops
- Model optimization
- Edge deployment
- Data augmentation
Education & Certifications
Master’s or PhD in Machine Learning, Computer Vision, Robotics, or related field preferred. Bachelor’s candidates considered on a case-by-case basis. History of academic publications in relevant ML fields is a plus.
Compensation & Benefits
Competitive salary range of $190,000 - $250,000 based on experience and location, plus comprehensive benefits package.