Senior Engineer I, AI/Data Science

Reposted 18 Days Ago
Headquarters, AZ, USA
In-Office
140K-184K Annually
Senior level
Information Technology • Biotech
The Role
The Senior Engineer II, Machine Learning will develop and implement machine learning models, analyze multiomic/single-cell data, and apply image processing techniques for research projects.
Summary Generated by Built In

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, AI/Data Science 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 image processing techniques to support 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:

  • Develop and implement deep learning models (CNNs, Vision Transformers, U-Net variants, and related architectures) for biological image analysis and classification, under the guidance of senior team members
  • Support deployment and maintenance of neural network models on cloud infrastructure (e.g., AWS) or directly on imaging instruments
  • Apply image processing and computer vision techniques to analyze multimodal biological images, including segmentation, feature extraction, and quality scoring
  • Contribute to 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 support experiment design and translate biological requirements into modeling objectives
  • Explore and analyze imaging datasets to identify patterns, failure modes, and opportunities for model improvement
  • Communicate findings and model performance metrics to stakeholders through reports and presentations
  • Partner with molecular biology, chemistry, and data-engineering teams to validate findings
  • Stay current with the latest advancements in deep learning, computer vision, and computational biology, and evaluate their applicability to internal research problems

Education and Experience:

  • PhD with 0–3 years of experience is strongly preferred or a minimum of Master’s degree in Computer Science, Electrical Engineering, Bioinformatics, Computational Biology, or a related field with 2–4 years of relevant experience
  • Experience developing deep learning models for image analysis is required; exposure to production or deployment environments (cloud-hosted or on-instrument) is a plus
  • Proficiency with deep learning architectures including CNNs, Vision Transformers (ViT), U-Net, or attention-based models
  • Experience with biological or biomedical image modalities (e.g., fluorescence microscopy, brightfield, high-content imaging) is a plus
  • Proficiency in Python and relevant deep learning and data science libraries: PyTorch, torchvision, OpenCV, Scikit-learn, NumPy, Pandas, and related tools
  • Familiarity with cloud computing platforms (e.g., AWS) is a plus
  • Proficiency in working with multiomic and single-cell data is a plus
  • Familiarity with bioinformatics and computational biology tools and databases is a plus
  • 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:

  • $127,000 - $155,000

 

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.

Skills Required

  • Master's degree in Data Science or related field
  • 5-7 years of relevant experience
  • Expertise in data science and machine learning techniques
  • Proficiency in Python and machine learning libraries
  • Experience with cloud computing platforms
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The Company
HQ: San Diego, CA
130 Employees
Year Founded: 2017

What We Do

Element Biosciences is a multi-disciplinary startup focused on innovating genetic analysis tools for the research and diagnostic markets. We are developing a modular and high-performing DNA sequencing platform that will deliver high-quality data, increase workflow flexibility, and make next-generation sequencing technology more accessible. Our proprietary approach to improving the signal-to-noise ratio allows us to provide groundbreaking innovations in surface chemistry, instrumentation, and biochemistry to drastically decrease run costs and capital costs while delivering high sequencing data quality.

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