Senior Engineer, Machine Learning

Elementbiosciences; San Diego (CA); On-site; USD 139,000 - 183,000; Full time

Benefits offered by this job

Stock options

Annual bonus

Health insurance

401k with match

Flexible PTO

Job summary

Element Biosciences in San Diego is seeking a Senior Engineer, Machine Learning to design and deploy DL models for biological image analysis and multiomic data. You will build end-to-end ML pipelines, apply computer vision techniques, and collaborate with biology and software teams in an onsite role.

The ideal candidate has 5–7 years of experience (or PhD with 0–3 years), strong Python, PyTorch, AWS, Docker, and a track record deploying models in production environments.

Qualifications

• Hands-on experience developing and deploying deep learning models for image analysis in production environments — either cloud-hosted or on-instrument — is required
• Master's or PhD with related field is expected
• Strong proficiency with modern deep learning architectures including CNNs, Vision Transformers, U‑Net, and attention-based models; familiarity with self-supervised or contrastive learning is a plus
• Experience with biological or biomedical image modalities is strongly preferred
• Proficiency in Python and libraries: PyTorch, torchvision, OpenCV, Scikit-learn, NumPy, Pandas
• Experience with cloud platforms (AWS), model serving, containerization (Docker), and GPU-accelerated compute
• Familiarity with model calibration, uncertainty quantification, or performance evaluation frameworks is a plus
• Experience with single-cell or multiomic data analysis tools and workflows is a plus
• Knowledge of experimental design and statistical analysis
• Strong background in statistics and ability to reason about model outputs quantitatively
• Excellent problem-solving skills and cross-disciplinary collaboration

Responsibilities

• Design, develop, and optimize deep learning models for biological image analysis and classification
• Deploy and maintain production-grade neural network models on cloud infrastructure or on instruments
• Apply advanced image processing and computer vision techniques to multimodal biological images
• Develop and manage end-to-end ML pipelines from data ingestion to inference
• Analyze single-cell and multiomic data to support imaging results interpretation
• Collaborate with biology, software engineering, and instrumentation teams to translate requirements
• Explore and analyze large-scale imaging datasets to identify improvement opportunities
• Communicate findings and model performance to stakeholders through reports and presentations
• Stay current with advances in DL, CV, and computational biology and assess applicability

Skills

Deep learning

CNNs

Vision Transformers

U‑Net

Image analysis

Python

PyTorch

OpenCV

AWS

Docker

GPU compute

Single-cell data

Multiomics

Statistical analysis

Education

Master's degree in Computer Science, Electrical Engineering, Bioinformatics, Computational Biology, or a related field

PhD with 0–3 years of experience

Tools

AWS

Docker

GPU acceleration

OpenCV

Job description

At Element Biosciences, we are passionate about our mission to empower the scientific community with more freedom and flexibility to accelerate our collective impact on humanity. We have built a highly efficient product-driven organization where employees can learn, grow, and thrive in a challenging but encouraging environment. We are committed to scientific integrity, collegiality, honesty, objectivity, and openness.

We are seeking a highly skilled and motivated Senior Engineer, Machine Learning to join our dynamic team. The ideal candidate will have experience in data science and machine learning, with a background in working with multiomic and single-cell data and/or image processing and computer vision. This role involves creating, exploring, and analyzing models, as well as applying advanced image processing techniques to drive our research and development efforts and contribute to critical programs. This role will report to Vice President, AI and will be an onsite role at our headquarters in San Diego.

If you possess the following and want to make a meaningful impact, we invite you to explore this role.

Essential Functions and Responsibilities

• Design, develop, and optimize deep learning models (CNNs, Vision Transformers, U-Net variants, and related architectures) for biological image analysis and classification
• Deploy and maintain production‑grade neural network models on cloud infrastructure (e.g., AWS) or directly on imaging instruments, ensuring reliability, scalability, and performance
• Apply advanced image processing and computer vision techniques to analyze multimodal biological images, including segmentation, feature extraction, and quality scoring
• Develop and manage end-to-end ML pipelines — from data ingestion and preprocessing through model training, validation, and inference
• Analyze and interpret single‑cell and multiomic data to support biological context and downstream interpretation of imaging results
• Collaborate with cross‑functional teams including biology, software engineering, and instrumentation to co‑design experiments and translate biological requirements into modeling objectives
• Explore and analyze large‑scale imaging datasets to identify patterns, failure modes, and opportunities for model improvement
• Communicate findings, model performance metrics, and technical trade‑offs to stakeholders through reports and presentations
• Stay current with the latest advances in deep learning, computer vision, and computational biology, and evaluate their applicability to internal research problems

Education and Experience

• Master's degree in Computer Science, Electrical Engineering, Bioinformatics, Computational Biology, or a related field with 5–7 years of relevant experience, or PhD with 0–3 years of experience
• Hands‑on experience developing and deploying deep learning models for image analysis in production environments — either cloud‑hosted or on‑instrument — is required
• Strong proficiency with modern deep learning architectures including CNNs, Vision Transformers (ViT), U‑Net, and attention‑based models; familiarity with self‑supervised or contrastive learning methods is a plus
• Experience with biological or biomedical image modalities (e.g., fluorescence microscopy, brightfield, high‑content imaging) is strongly preferred
• Proficiency in Python and relevant deep learning and data science libraries: PyTorch, torchvision, OpenCV, Scikit‑learn, NumPy, Pandas, and related tools
• Experience with cloud computing platforms (e.g., AWS), including model serving, containerization (Docker), and GPU‑accelerated compute
• Familiarity with model calibration, uncertainty quantification, or performance evaluation frameworks is a plus
• Experience with single‑cell or multiomic data analysis tools and workflows is a plus (not required)
• Knowledge of experimental design and statistical analysis
• Strong background in statistics and comfort reasoning about model outputs quantitatively
• Excellent problem‑solving skills, attention to detail, and ability to work across scientific and engineering disciplines

Physical Requirements

• Frequently moves boxes weighing up to 20 pounds

Location

• San Diego – on‑site

Travel

• Domestic travel up to 10%

Job Type

• Full‑time/Exempt

Base Compensation Pay Range

• $139,000 - $183,000

In addition to base compensation noted above, you will be eligible for stock options, discretionary annual bonus, no cost health insurance plans, 401k with company match, and flexible paid time off.

Please note: Base compensation will depend on multiple factors, including geographic location, qualifications, and experience.

We foster an environment such that all people are afforded the freedom to pursue their passions without regard to race, color, religion, national or ethnic origin, gender (including pregnancy), sexual orientation, gender identity or expression, age, disability, veteran status or any other characteristics protected by law.