Senior CV/ML Scientist

Jobtailor; San Diego (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Jobtailor in San Diego seeks a research scientist to advance state‑of‑the‑art computer vision and deep learning models for detection, segmentation, tracking, and vision‑language tasks. You will translate research into working implementations and reproduce baselines, run ablations, and quantify tradeoffs for production use.

You will own the full data lifecycle for visual tasks, including dataset curation, annotation strategy, augmentation, synthetic data, and quality analysis, and collaborate

Qualifications

• Master’s or PhD in CS/EE/Robotics/ML, or Bachelor’s with equivalent research/industry experience.
• Strong foundation in CV and DL: classification, detection, segmentation, tracking across CNNs, Vision Transformers, and vision-language models.
• Solid DL fundamentals: supervised/self-supervised learning, representation learning, optimization, loss design, and evaluation.
• Ability to read, implement, and build on recent papers — turning literature into working prototypes.
• Hands-on experience with real-world visual data: dataset curation, annotation, augmentation, synthetic data, data‑quality analysis.
• Proficiency in Python with PyTorch or equivalent DL framework; strong applied mathematics.
• Strong collaboration and communication skills across research, engineering, and product teams.
• Nice to Have: reproduce and extend recent research with careful baselines and ablations.
• Experience in robotic vision, vision-language-action systems, world models, or generative models for images/videos.
• Familiarity with 3D geometry, camera models, calibration.
• Publications/open-source contributions or substantial project experience in CV/Robotics.

Responsibilities

• Research, prototype, and develop state-of-the-art CV and DL models for detection, segmentation, tracking, and vision-language tasks.
• Translate research into working implementations — reproduce baselines, run ablations, quantify practical tradeoffs.
• Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation, synthetic data generation, and quality analysis.
• Collaborate with engineering and product teams to move models from prototype to production, including edge/latency optimization.
• Communicate methods, results, and tradeoffs clearly to both technical and non-technical stakeholders.

Skills

Computer vision

Deep learning

Python

PyTorch

Edge deployment

Education

Master’s or PhD in CS/EE/Robotics/ML

Tools

PyTorch

ONNX

TensorRT

Job description

• Research, prototype, and develop state-of-the‑art computer vision and deep learning models spanning detection, segmentation, tracking, and vision‑language tasks.
• Translate recent research into working implementations — reproducing baselines, running ablations, and quantifying practical tradeoffs.
• Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation, synthetic data generation, and quality analysis.
• Collaborate closely with engineering and product teams to move models from prototype to production, including optimization for edge or latency‑constrained environments.
• Communicate methods, results, and tradeoffs clearly to both technical and non‑technical stakeholders.

Requirements

• Master’s or PhD in Computer Science, Electrical Engineering, Robotics, Machine Learning, or a related field — or a Bachelor’s with equivalent research or industry experience.
• Strong foundation in modern computer vision and deep learning: image classification, object detection, segmentation, and tracking across CNNs, Vision Transformers, vision‑language models, and foundation models.
• Solid grasp of deep learning fundamentals: supervised and self‑supervised learning, representation learning, optimization, loss design, and rigorous model training and evaluation.
• Demonstrated ability to read, implement, and build on recent research papers — turning literature into working prototypes.
• Hands‑on experience with real‑world visual data: dataset curation, annotation, augmentation, synthetic data, data‑quality analysis, and low‑data or noisy‑data settings.
• Strong applied mathematics (linear algebra, probability, statistics, optimization) and proficiency in Python with PyTorch or equivalent deep learning framework.
• Strong collaboration and communication skills — able to work across research, engineering, and product, and present technical work clearly to mixed audiences.
• Nice to Have: Proven ability to reproduce, evaluate, and extend recent research, including careful baseline comparisons, ablation studies, and tradeoff analysis.
• Experience in robotic vision, vision‑language‑action systems, world models, imitation learning, reinforcement learning, or generative models for image, video, or simulation.
• Familiarity with 3D geometry, camera models, coordinate frames, and calibration.
• Publications at top‑tier venues, open‑source contributions, research internships, or substantial project experience in computer vision, robotics, or embodied AI.
• Hands‑on experience deploying or optimizing models for production or edge environments — model compression, quantization, ONNX, TensorRT, or latency optimization.