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Overview
Replimune’s mission is to revolutionize cancer treatment with therapies designed to activate a powerful and durable full\-body anti\-tumor response, boldly transforming cancer care because patient’s lives depend on it.
At Replimune, we live by our values:
United: We Collaborate for a Common Goal.
Audacious: We Are Bold.
Dedicated: We Give Our Full Commitment.
Candid: We Are Honest With Each Other.
People are at the center of everything we do, and when it comes to our employees, we make it personal. With a deep sense of purpose, a strong and evolving culture, a competitive and forward\-looking total rewards program, everyone at Replimune has a unique opportunity to impact on the lives of patients, caregivers and themselves.
Join us, as we reshape the future.
Summary of Job Description:
The Associate Director of CMC Data Science will work with cross\-functional teams while applying the appropriate science\-based techniques to:
- support / creation of cGMP process monitoring capabilities
- design statistically sound lab\-based experiments
- solve operations and business problems
- support integration AI into CMC workflows in a compliant manner
- manage the CMC Data Science team
*This position is based in our Framingham location and typically has a 5\-day on\-site expectation.*
Key Responsibilities:
- CMC Statistical Lead and point of contact for statistical analysis requests across the CMC organizations
- Provide technical expertise and support to serve as an authority in GMP process monitoring (CPV) and key contributor to commercial annual product reports.
- Working with IT, serve as a team leader / project lead for the creation of data repository for all CMC data (e.g. electronic data warehouse)
- Identify data flow issues working with end customers to understand needs and deliver short term and long\-term solutions to help scientist/engineers/managers complete their deliverables in an agile manner
- Working with site / group leaders create tools to better visualize potential risks and issues to critical CMC workflows
- Work with cross functional customers to embed AI\-enabled CMC business processes into existing workflows to improve efficiency, decision\-making, and innovation
- Create / advise on DOEs to be used within lab\-based experiments supporting both drug substance and drug product production.
- Drive continuous improvement in our cGMP process steps by identifying process improvements
- Actively contribute to cross functional teams to achieve production site and company goals in support of commercial production readiness
- Manage the members of the data science group to perform similar key responsibilities
- Ensure that all data science team members comply with the data governance strategy for operations to better enable data consumers to find, access, and use data
Other Responsibilities:
- Practices and promotes safe work habits and adheres to safety procedures and guidelines.
- Provide technical representation during internal and external audits.
- Keeps up to date with current technologies and trends in biologics manufacturing operations and CMC data trending / warehousing / visualization tools and applications.
- Additional duties and responsibilities as required
Educational Requirements:
- Bachelor’s degree in applicable field with advanced degree preferred
Experience and Skill Requirement:
- Minimum of 4 year working in data science role, preferably in support of biologics CMC.
- Expert in statistical analysis, predictive modelling, and data visualization tools
- Experience with at least one data management system (data lake, data warehouse, or data lake house)
- Experience with ML/AI integration into CMC workflow(s)
- Demonstrated ability to work in cross\-functional teams across the business.
- Strong organizational skills and attention to detail.
- Experience as a people manager
- Excellent written and oral communication skills and strong team player
Salary Range
Replimune is committed to fair and equitable compensation practices, and we strive to provide employees with total compensation packages that are market competitive. For this role, the anticipated base pay range is $160k\-$207,500The exact base pay offered for this role will depend on various factors, including but not limited to the selected candidate’s unique set of qualifications, skills, and experience. Our current organizational needs also play a part in determining your final offer.
At Replimune, base pay is only one part of your total compensation package. The selected candidate will be eligible for an annual performance incentive bonus, new hire equity, and ongoing performance\-based equity. Replimune also offers various benefits offerings, including, but not limited to, medical, dental, and vision insurance, 401k match, flexible time off, and a number of paid holidays including year\-end shutdown.
About Replimune
Replimune Group, Inc., headquartered in Woburn, MA, was founded in 2015 with the mission to transform cancer treatment by pioneering the development of a novel portfolio of oncolytic immunotherapies. Replimune’s proprietary RPx platform is based on a potent HSV\-1 backbone intended to maximize immunogenic cell death and the induction of a systemic anti\-tumor immune response. The RPx platform is designed to have a unique dual local and systemic activity consisting of direct selective virus\-mediated killing of the tumor resulting in the release of tumor derived antigens and altering of the tumor microenvironment to ignite a strong and durable systemic response. The RPx product candidates are expected to be synergistic with most established and experimental cancer treatment modalities, leading to the versatility to be developed alone or combined with a variety of other treatment options. For more information, please visit www.replimune.com.
We are an Equal Opportunity Employer.
Salary Context
This $160K-$207K range is above 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 Replimune, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($183K) sits 16% below the category median. Disclosed range: $160K to $207K.
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.
Replimune AI Hiring
Replimune has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Framingham, MA, US. Compensation range: $207K - $207K.
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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