Senior AI/Machine Learning Engineer, Evolv Technology

• Master’s or PhD in Computer Science, Machine Learning, Engineering, Applied Math, Physics, or related field
• 3- 5+ years building and deploying ML models for real-world applications
• Strong expertise in classical ML techniques (e.g., XGBoost, Random Forests, SVM, k‑NN) and modern ML techniques (e.g., deep neural network, transformers)
• Proficiency in Python, ML libraries (scikit‑learn, NumPy, pandas) and C++
• Experience with multi‑class classification on real‑world, noisy datasets
• Strong statistical and model evaluation skills

Desirable

• Join Evolv as Senior AI/Machine Learning Engineer to advance AI innovation in physical security technology
• As a key team member of the AI/ML team, you will be developing and deploying state-of-the-art machine learning and deep learning solutions
• Your role will involve leveraging diverse data sources, including magnetic sensors, 3D cameras and other sensors, to create multi-sensor fusion solutions that operate in real-time on constrained hardware platforms
• This hands-on role requires deep expertise in classical ML, deep learning, feature engineering, model optimization, and MLOps
• You will drive modeling strategy, strengthen model accuracy and robustness, and deploy reliable models in real-world environments
• This position is ideal for someone known for measurably improving models—not just building them
• In the first 30 days:
• Learn the sensor ecosystem, ML pipelines, and development standards
• Review real-time constraints, production workflows, and existing model performance baselines
• Engage in code reviews and collaborate across engineering teams
• Identify key opportunities for improving accuracy, latency, and robustness
• Within the first three months:
• Lead feature engineering from raw sensor inputs, including temporal, spectral, and statistical features
• Develop and optimize classical ML and deep learning models
• Propose model improvements through systematic experimentation and benchmarking
• Partner with product and hardware teams to translate sensor behavior into ML architectures
• By the end of the first year:
• Own end‑to‑end ML model lifecycle for core production systems
• Deploy scalable ML models and ensure operational reliability
• Drive architecture decisions balancing classical ML and deep learning approaches
• Improve robustness across devices and field environments by modeling sensor characteristics
• Design, develop, and optimize ML models—including XGBoost, Random Forests, SVMs, CNNs, and Transformers
• Lead hyperparameter tuning, feature selection, and algorithm evaluation
• Integrate models to production system, work with SW team on optimizing runtime speed and performance
• Develop reproducible training pipelines with model, data, and experiment versioning
• Feature Engineering & Sensor‑Aware Modeling
• Extract temporal, spectral, and domain‑specific features from raw sensor signals
• Use data analytics tools such as UMAP and T-SNE to understand data distribution and feature characteristics
• Model sensor characteristics such as noise, bias, drift, and environmental effects
• Perform ablation studies and feature importance analyses (SHAP, PDP, etc.)
• Multi‑Class Detection & Classification:
• Design multi‑class object detection and classification pipelines for noisy, imbalanced datasets
• Define evaluation metrics including confusion matrices, calibration, and class‑wise scoring
• MLOps & Production Excellence:
• Deploy production‑ready ML code impacting real customers
• Ensure reliability through CI/CD, drift detection, and data validation
• Optimize models for edge and compute‑constrained environments
• Cross‑Functional Collaboration:
• Work with hardware, software, product and cross-functional teams
• Communicate technical decisions and trade‑offs to senior stakeholders
• Example Problems You Will Own:
• Redesign a multi‑class sensor‑based classification pipeline for improved accuracy, stability, and inference speed
• Develop ML architectures that explicitly model sensor behavior and failure modes
• Build a comparative framework for evaluating classical ML vs. deep learning models
• Own the end‑to‑end lifecycle of a core production ML system
• You will join our R&D organization, reporting directly to VP of ML and sensors
• In this role, you will interface with cross-disciplinary teams of highly skilled and autonomous engineers with expertise in Electromagnetics, Computer Vision, and AI
• Our R&D organization includes more than 100 dedicated developers, engineers, scientists, managers and directors, each bringing deep technical knowledge and a strong culture of collaboration and support
• The team culture is one based on building trust, collaboration, on-going development through kindness, authenticity, courage, drive, and fun!
• This role is based at our headquarters in Waltham, Massachusetts. Due to the nature of our software-enabled hardware products, this position requires a minimum of 60% or 3 days on-site work