Interested in this AI/ML Engineer role at Arrowhead Pharmaceuticals?
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About This Role
Arrowhead Pharmaceuticals, Inc. (Nasdaq ARWR) is a commercial stage biopharmaceutical company that develops medicines that treat intractable diseases by silencing the genes that cause them. Using a broad portfolio of RNA chemistries and efficient modes of delivery, Arrowhead therapies trigger the RNA interference mechanism to induce rapid, deep, and durable knockdown of target genes. RNA interference, or RNAi, is a mechanism present in living cells that inhibits the expression of a specific gene, thereby affecting the production of a specific protein. Arrowhead's RNAi-based therapeutics leverage this natural pathway of gene silencing.
Arrowhead is focused on developing innovative drugs for diseases with a genetic basis, typically characterized by the overproduction of one or more proteins that are involved with disease. The depth and versatility of our RNAi technologies enables us to potentially address conditions in virtually any therapeutic area and pursue disease targets that are not otherwise addressable by small molecules and biologics. Arrowhead is leading the field in bringing the promise of RNAi to address diseases outside of the liver, and our clinical pipeline includes disease targets in the liver and lung with a promising pipeline of preclinical candidates.
Arrowhead's corporate headquarters is in Pasadena, CA with research and development teams in Madison, WI & San Diego, CA, and a state of the art manufacturing facility in Verona, WI. Our employees are nimble, science-driven innovators who are collaborating to bring new therapies to patients in need.
The Position
This is a newly created, green-field role and the first Forward Deployed Engineer, AI hired within the company. The person in this role will partner directly with business and scientific stakeholders across multiple functions to identify high-value problems and build and deploy AI-driven solutions that solve them. Because there is no existing playbook for this function, the Forward Deployed Engineer will help define how AI solutions are scoped, built, and delivered internally, working hands-on across the AWS, Azure, Databricks, and Microsoft technology stack alongside the Data Platform Engineer, DevOps Engineer, and Databricks/AWS platform teams. This role requires someone comfortable operating with ambiguity, moving quickly from problem discovery to a working solution, and embedding directly with internal teams to understand their workflows before proposing and shipping AI tools that fit how they actually work.
Responsibilities
- Partner directly with stakeholders across R&D, clinical, manufacturing, commercial, and other business functions to identify, scope, and prioritize opportunities for AI-driven internal solutions.
- Design, build, and deploy agentic AI workflows and applications that solve real operational and scientific problems, working end-to-end from prototype through production.
- Establish foundational engineering practices, standards, and reusable patterns for AI solution delivery, since this is the first role of its kind in the organization.
- Work closely with the Data Platform Engineer to access, model, and productionize data needed for AI solutions across Databricks and AWS.
- Collaborate with the DevOps Engineer to build reliable CI/CD pipelines, deployment processes, and monitoring for AI applications across AWS/ Databricks inside a MS environments.
- Leverage the Databricks platform and AWS services (e.g., Bedrock, Lambda, S3) to build and orchestrate agentic AI workflows and applications, primarily by composing and integrating existing foundation models rather than training new ones.
- Integrate solutions with Microsoft/Azure tools (e.g., Azure AI services, M365) where appropriate to meet business needs.
- Rapidly prototype proof-of-concepts with internal teams, gather feedback, and iterate quickly to move validated solutions into production.
- Document architecture, decisions, and delivery patterns to build a repeatable internal playbook for future forward-deployed engineering work.
- Act as a hands-on technical resource embedded with business teams, translating ambiguous business problems into concrete technical solutions.
Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
- 5+ years of professional software engineering experience, including building and deploying production-grade applications.
- Hands-on experience with AWS cloud services and Azure/Microsoft cloud services.
- Experience working with Databricks, including data engineering, notebooks, and/or ML workflows.
- Strong proficiency in at least one modern programming language (e.g., Python, SQL, or similar) used for building data and AI applications.
- Demonstrated experience building agentic workflows and applications with generative AI and LLM-based approaches (e.g., orchestration frameworks, tool use, retrieval-augmented generation) to solve practical business problems.
- Proven ability to work independently in ambiguous, undefined environments and to build solutions from the ground up without an established playbook.
- Strong cross-functional communication skills, with the ability to translate technical concepts for non-technical business stakeholders and vice versa.
Preferred
- Knowledge of the pharmaceutical, biotech, or life sciences industry is desirable but not required.
- Experience with Databricks certifications, AWS certifications, or Microsoft Azure certifications.
- Prior experience in a "forward deployed engineer," solutions engineering, or embedded engineering role, working directly with business teams rather than in a purely centralized product function.
- Experience with LLMOps/agent-ops tooling and practices for deploying, monitoring, and evaluating agentic workflows in production.
- Familiarity with regulated-industry considerations (e.g., data privacy, validation, GxP) relevant to deploying AI solutions in a pharma environment.
- Experience building internal tools, chatbots, or agentic AI applications using modern LLM frameworks.
- A builder mindset with the initiative to define scope and process in a green-field function.
Arrowhead provides competitive salaries and an excellent benefit package.
Candidates must have current, valid authorization to work in the country where this role is located.
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Salary Context
This $155K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Arrowhead Pharmaceuticals, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $155K to $175K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Arrowhead Pharmaceuticals AI Hiring
Arrowhead Pharmaceuticals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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