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About This Role
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.
Salary Context
This $139K-$183K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Element Biosciences Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($161K) sits 26% below the category median. Disclosed range: $139K to $183K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Element Biosciences Inc AI Hiring
Element Biosciences Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $183K - $183K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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