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About This Role
Position Overview:
Position Description
We are hiring a Director Data, AI and Analytics. The Director Data, AI and Analytics will own the strategy, platforms, operating model, and outcomes for Data, AI, and Analytics across three FUJIFILM life‑sciences businesses. In highly regulated GxP environments, deliver a trusted data backbone, governed self‑service analytics, advanced analytics, bioinformatics pipelines, and agentic AI—meeting 21 CFR Part 11/EU Annex 11, ALCOA\+, and CSV/CSA expectations. Lead a cross‑site team (CoE \+ domain squads), partner with QA/CSV, Security, Lab IT, M\&Q Systems, and ERP, and convert data and AI into measurable business value. This position is hybrid in Irvine, CA.
Company Overview:
At FUJIFILM Biosciences, we turn curiosity into breakthroughs that advance the field of life sciences. By offering a comprehensive portfolio of products and services in cell culture media, discovery research reagents, recombinant growth factors and proteins, fine chemicals, and critical assay materials, we partner with the brightest minds in biotech and pharma to tackle the world’s biggest health challenges.
Imagine being part of a team that enables life\-changing discoveries like new vaccines, therapies, and advancements in regenerative medicine. Our culture fosters curiosity, collaboration, and innovation, and we pride ourselves on delivering unparalleled quality and service to our partners. If you’re ready to make an impact, your future belongs with us at FUJIFILM Biosciences.
Our headquarters in Santa Ana, California, is surrounded by world\-class schools, lush parks, and scenic beaches like Laguna and Huntington, making it an ideal place to live, work, and explore. With additional campuses across the US, Europe, China, and Japan, we offer opportunities to make a difference worldwide.
Fujifilm is globally headquartered in Tokyo with over 70,000 employees across four key business segments of healthcare, electronics, business innovation, and imaging. We are guided and united by our Group Purpose of “giving our world more smiles.” Visit: https://www.fujifilm.com/us/en/about/region/careers
Job Description:
Responsibilities:
- Define the multi‑year Data/AI roadmap for locations in California and Wisconsin; align to business priorities and compliance needs.
- Stand up a Data, AI \& Analytics Center of Excellence with domain‑aligned product squads (M\&Q, R\&D/Bioinformatics, Finance/SC).
- Run governance: Data \& AI Steering Committee, Model Risk \& Compliance Board; assign data product ownership.
- Own the enterprise data platform (lakehouse/warehouse), ingestion (ELT/CDC), orchestration, and semantic/metric layers.
- Implement catalog/lineage, data quality rules, access controls; drive data product SLAs and ownership.
- Integrate core systems: MES (e.g., PAS‑X), QMS (ETQ/Veeva), LIMS/ELN (LabWare/Benchling), ERP (e.g., SAP/Oracle), OSI PI/AVEVA, and instrument data hubs.
- Deliver executive and operational analytics (FPY/yield, OEE, deviation/CAPA TAT, batch release, QC throughput, inventory turns, cost‑to‑serve).
- Enable governed self‑service (certified datasets/semantic models, KPI library, training, community of practice).
- Lead data science programs for manufacturing quality, supply chain, and R\&D.
- Own bioinformatics analytics and pipelines (e.g., NGS/omics workflows, imaging pipelines, assay analytics) and integration with lab systems and data platforms.
- Build responsible and agentic AI capabilities (assistants/co‑pilots, retrieval/grounding, task automation) with MLOps (experiment tracking, model registry, CI/CD, monitoring, retraining). Separate GxP vs non‑GxP AI; ensure intended use, explainability, validation/CSA, and periodic review for regulated models.
- Embed compliance by design: 21 CFR Part 11, Annex 11, ALCOA\+, data retention/archival, audit trails.
- Partner with QA/CSV on risk‑based validation, test evidence, and inspections; maintain validated state for applicable analytics/data flows.
- Coordinate with Corporate Security on classification, RBAC/least privilege, encryption, logging/monitoring, and incident response for data/AI workloads.
- Govern AI safety: model cards, red‑team testing, bias/fairness checks, PHI/PII safeguards, vendor due diligence.
- Prioritize a portfolio of use cases with quantified value; pilot, scale winners, retire low‑value work.
- Publish a quarterly “Data \& AI Outcomes” report (business impact, adoption, cost/performance, compliance posture).
- Select and govern platform vendors (cloud, data/ML, BI, bioinformatics tools) and SIs; negotiate commercials, SLAs, exit terms.
- Drive FinOps: usage visibility, rightsizing, tiered storage, and cost/performance optimization.
- Hire and develop data/platform/analytics engineers, data scientists/ML engineers, bioinformatics engineers, and product managers.
- Build cross‑site career paths and a culture of reliability, compliance, and measurable impact.
Required Skills/Education:
- BS/BA (or equivalent) in Business, Computer Science or similar.
- 10\+ years leading Data/Analytics functions with 5\+ years in regulated life sciences (biotech, pharma, CDMO); multi‑site/global experience.
- Ownership of enterprise data platforms (lakehouse/warehouse), analytics programs, and MLOps in production.
- Strong GxP/CSV/CSA knowledge with practical application in analytics/AI and data flows; 21 CFR Part 11/Annex 11/ALCOA\+.
- Hands‑on familiarity with bioinformatics analytics/pipelines (e.g., NGS/omics, imaging, workflow orchestration) and integration with LIMS/ELN and lab systems.
- Proven record converting Data/AI programs into business outcomes (e.g., cycle time, yield/FPY, cost, quality).
- Cross‑functional leadership with QA/CSV, Manufacturing/Quality, R\&D, Security, ERP/MES/QMS owners, and vendors.
- Excellent executive communication and pragmatic technical guidance.
- Platforms: Databricks/Snowflake/BigQuery; Azure/AWS data services; Spark; DBT; Airflow/Prefect; Kafka/EventHub.
- BI: Power BI/Tableau; semantic/metrics layers; KPI governance.
- MLOps/AI: MLflow/W\&B, SageMaker/Azure ML; vector DBs/RAG; LLM orchestration; agent frameworks; feature stores.
- Bioinformatics: Nextflow/Snakemake, CWL/WDL; common tools (e.g., GATK) or imaging/omics pipelines; HPC/containers.
- Governance: Collibra/Alation/Atlan; model risk management/AI governance.
- Certifications: GAMP 5/CSA, ITIL, cloud (Azure/AWS), or data governance certifications.
- Ability to speak, read, and write in English.
- Good verbal and written communication skills; effective presentation skills.
Salary and Benefits:* For California, the base salary range for this position is $154,565\.00 \- $216,100\. Compensation for the successful candidate will depend on various factors (e.g., qualifications, education, prior experience, location, etc.).
- Medical, Dental, Vision
- Life Insurance
- 401k
- Paid Time Off
- \#LI\-hybrid
EEO Information:
Fujifilm is committed to providing equal opportunities in hiring, promotion and advancement, compensation, benefits, and training regardless of nationality, age, gender, sexual orientation or gender identity, race, ethnicity, religion, political creed, ideology, national, or social origin, disability, veteran status, etc.
ADA Information:
If you require reasonable accommodation in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to our HR Department ([email protected]).
Salary Context
This $154K-$216K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FujiFilm, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $154K to $216K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FujiFilm AI Hiring
FujiFilm has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $216K - $216K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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