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About This Role
Iovance Biotherapeutics aims to be the global leader in innovating, developing and delivering tumor infiltrating lymphocyte (TIL) therapy for people with cancer. We are pioneering a transformational approach to treating cancer by harnessing the ability of the human immune system to recognize and attack diverse cancer cells in each patient. The Iovance TIL platform has demonstrated promising clinical data across multiple solid tumors. We are committed to continuous innovation in cell therapy, including gene\-edited cell therapy, which may be a promising option for patients with cancer.
Overview
This is a senior role responsible for the hands\-on design, build, validation, and deployment of Artificial Intelligence (AI) systems at Iovance Biotherapeutics. This role is focused on execution — turning approved use cases into working, validated, production AI systems that deliver measurable business value. This role directly supports Iovance’s strategy to improve overall operational productivity. The ideal candidate is a deeply technical, hands\-on senior engineer with demonstrated production experience designing and shipping Large Language Model (LLM) applications, Retrieval\-Augmented Generation (RAG) systems, and modern AI integrations. This position works closely with IT leads, business process owners across Manufacturing, Quality, Regulatory Affairs, Clinical, Commercial, G\&A, IT Security, Privacy, and Quality Assurance to deliver AI capabilities that respect Iovance’s regulated environment. This role executes against a roadmap and prioritized backlog, while contributing technical input to refinement, scoping, and sequencing.
Primary Responsibilities
- Design, build, and ship AI systems against the approved Iovance AI roadmap, including end\-to\-end ownership of architecture, retrieval pipelines, prompts, evaluation, integrations, and deployment for assigned use cases.
- Implement production\-grade Retrieval\-Augmented Generation (RAG) systems on Iovance’s AWS infrastructure (S3, Redshift, Bedrock), including chunking strategies, embedding selection, vector storage, retrieval and reranking, grounding, and citation handling appropriate to high\-accuracy use cases.
- Build, maintain, and run evaluation harnesses for AI systems, including held\-out test sets, accuracy and grounding metrics, hallucination detection, adversarial inputs, and regression testing across model and prompt changes; treat evaluation as a first\-class engineering deliverable, not an afterthought.
- Design and implement integrations between AI systems and Iovance enterprise systems using Model Context Protocol (MCP), APIs, and event\-driven patterns, applying least\-privilege access principles and partnering with IT Security on integration approval.
- Own and maintain LLM security controls for production AI systems, including input and output guardrails, prompt injection and jailbreak defenses, sensitive data redaction (PII, PHI, Iovance Confidential Information and Intellectual Property), content moderation, and abuse monitoring, working in partnership with IT Security.
- Design, develop, deploy, and maintain AI agents (multi\-step reasoning systems that combine LLMs with tools, retrieval, and planning) appropriate for use in a regulated life sciences environment, including bounded scope, defined human oversight, traceability of agent decisions, and safe handling of write\-back actions to systems of record
- Establish and uphold modern engineering practices for AI development including version control for code, prompts, and evaluation sets; CI/CD pipelines; environment separation (dev, test, production); and reproducible builds.
- Conduct hands\-on technical evaluation of AI vendors, tools, and Foundation Models when build\-vs\-buy decisions are under consideration; produce concise, fact\-based recommendations to the IT function lead and AI Governance Committee, including proof\-of\-concept results where appropriate.
- Implement and operate technical controls for production AI systems including audit logging, access management, prompt and model change control, model registry, ongoing performance monitoring, and incident detection, in alignment with Iovance policies.
- Author technical documentation appropriate to the system risk tier, including architecture diagrams, data flow diagrams, evaluation reports, runbooks, and validation deliverables; contribute to the Iovance AI Validation Playbook.
- Mentor junior engineers who collaborate on AI projects; stay current on rapid advances in AI tooling, models, and engineering best practices, and bring technical recommendations forward.
Qualifications
- 10\+ years of progressive software and/or AI/ML engineering experience, with the most recent 1\+ years dedicated primarily to LLM\-based application development. Demonstrated track record of shipping production AI systems that real users depend on.
- Deep, hands\-on production experience with Retrieval\-Augmented Generation (RAG) including chunking strategies, embedding models, vector stores, retrieval and reranking, and grounding for high\-accuracy use cases. Working production experience with Model Context Protocol (MCP) or equivalent agent/tool integration patterns.
- Practical working experience across multiple frontier model families (e.g., Anthropic Claude, OpenAI GPT, Google Gemini, leading open\-weight models), with the judgment to select among them based on task fit, accuracy, cost, latency, safety properties, and data\-handling commitments. Awareness of model capability changes and how to evaluate new model releases.
- Strong software engineering fundamentals: production Python (and ideally one other language); modern Agile delivery; version control (Git); CI/CD; containerization; cloud platforms (preferably AWS, with working familiarity with S3, Redshift, and Bedrock); MLOps tooling and practices.
- Hands\-on, builder disposition; this role spends the majority of its time writing code, designing systems, evaluating models, and producing technical artifacts — not in meetings.
- Working production experience with AWS data and AI services is strongly preferred (S3, Redshift, Bedrock, IAM).
- Working knowledge of enterprise search and retrieval services such as OpenSearch, Elasticsearch, or equivalent, including the design of hybrid retrieval patterns (vector plus lexical/BM25\) that improve grounding and relevance in production RAG systems
Preferred/Desirable Knowledge, Skills, and Education
- Relevant cloud and AI/ML certifications (AWS, Azure, etc.);
- Working knowledge of US regulatory requirements applicable to AI in regulated life sciences, including 21 CFR Part 11, GxP, HIPAA, and emerging FDA expectations on AI/ML in pharmaceutical manufacturing and drug development
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related quantitative discipline.
Physical Demands and Activities Required
- Must be able to remain in a stationary position standing or sitting for prolonged periods of time.
- Must be able to move about inside an office and exert up to 10 pounds of force occasionally or a negligible amount of force frequently or constantly to lift, carry, push, pull, or otherwise move objects.
- Must have visual acuity to perform activities such as: preparing and analyzing data and figures, viewing a computer screen, and extensive reading.
- This position requires repetitive motion, substantial movements (motions) of the wrist, hands, and/or fingers.
- Must be able to communicate with others to exchange information.
Mental: Clear and conceptual thinking ability; excellent judgment, troubleshooting, problem\-solving, analysis, and discretion; ability to handle work\-related stress; ability to handle multiple priorities simultaneously; and ability to meet deadlines.
Work Environment
This job operates in a professional workplace or remote office environment and requires standard office equipment and keyboards. Employees who work remotely are expected to maintain their workspace and environment safely and free from safety hazards.
The annual base salary we reasonably expect to pay is listed. Individual pay decisions depend on various factors, such as primary work location, complexity and responsibility of the role, job duties/requirements, and relevant education, experience and skills.
Pay Transparency
$175,000 \- $200,000 USD
The statements contained in this document are intended to describe the general nature and level of work being performed by a colleague assigned to this description. They are not intended to constitute a comprehensive list of functions, duties, or local variances. Management retains the discretion to add or to change the duties of the position at any time.
Iovance is committed to cultivating and offering a diverse and inclusive work environment. As an equal\-opportunity employer, our employees and applicants will be considered without regard to an individual’s race, color, religion, sex, pregnancy, national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information, military and veteran status, and any other characteristic protected by applicable law. If you need assistance or accommodation to apply to one of our opportunities, please contact [email protected].
Salary Context
This $175K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Iovance Biotherapeutics, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($187K) sits 5% above the category median. Disclosed range: $175K to $200K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Iovance Biotherapeutics AI Hiring
Iovance Biotherapeutics has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $125K - $200K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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