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About This Role
Job Title: 2027 Spring Co-op Biologics Design and AI
- Grade: Co-op
- Location: Cambridge, MA
- Duration: 6 months
About the Job
Looking to launch your career at the cutting edge of healthcare? Join Sanofi for a chance to develop with mentoring and guidance from inspirational leaders while helping to make an impact on the lives of countless people worldwide. We are seeking a highly motivated Spring Co-op to join our Biologics Design and AI team.
Sanofi is seeking a highly motivated and intellectually curious Data Scientist Spring Co-op to join our Biologics and Design AI team. In this role, you will be at the forefront of a highly innovative project: bridging the world of Large Language Models (LLMs) and autonomous AI agents with biophysical protein predictive models.
Biologics like antibodies and nanobodies are highly effective therapies but optimizing them is a complex challenge. You will closely work with computational biologists and AI scientists to build intelligent, agentic workflows that can reason over structural data, orchestrate predictive tools, and map protein sequences to critical therapeutic properties, ultimately accelerating our biologics pipeline.
Ready to grow your skills while helping shape the future of healthcare? At Sanofi, you’ll be empowered to learn, ask questions, and bring your ideas to life – all while supported by inspiring mentors and collaborative teams.
About Sanofi
We’re an R&D-driven, AI-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.
Main Responsibilities
- LLM Fine-Tuning & Cross-Modal Adaptation: Fine-tune conversational, natural language LLMs to directly interface with and reason over data from specialized protein prediction models. Using parameter-efficient techniques (e.g., LoRA, PEFT), you will adapt these human-language models to comprehend protein sequences, structural embeddings, and biophysical constraints. By bridging the gap between human-like generative AI reasoning and rigorous structural biology, you will build hybrid architectures designed to significantly enhance the accuracy of critical antibody and nanobody property predictions, such as binding affinity and thermal stability.
- Collaboration & Reporting: Partner closely with experimental protein engineers and computational biologists to understand biological constraints, define the agent's objectives, and validate model predictions, ultimately presenting your methodologies, insights, and model performance to cross-functional R&D teams at the conclusion of the spring term.
About you
Basic Qualifications:
- Currently pursuing a Master’s or PhD degree in Computer Science, Data Science, Computational Biology, Bioinformatics, Bioengineering, or related field with plans to graduate by Spring 2027 (or 2028).
- Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future. Students currently on CPT, OPT, or STEM OPT usually require future sponsorship for long term employment and do not meet the Internal requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship.
- Must be enrolled in an accredited college or university throughout the duration of the co-op/internship.
- Must be able to relocate to the office location and work 40hrs/week, Monday to Friday for the full duration of the co-op/internship.
Preferred Qualifications:
- Previous academic projects or industry experience demonstrating the application of NLP, LLMs, or agentic workflows to complex, ideally scientific, problems.
- Familiarity with biological data, specifically protein sequences and 3D structures.
- Basic domain knowledge in immunology, structural biology, or biophysics.
- Strong programming skills in Python and standard data science libraries.
- Hands-on experience with LLM fine-tuning methodologies and orchestration frameworks.
- Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Experience using state-of-the-art AI tools for biology (e.g., Protein LLMs, AlphaFold, Rosetta).
- Excellent problem-solving communication skills, with a passion for working at the intersection of generative AI and healthcare.
- Fluent in English.
Why Choose Us
- Bring the miracles of science to life alongside a supportive, future focused team.
- Stretch your skills through apprenticeships, internships, graduate programs, and international experiences that grow with you.
- Learn from inspiring mentors and collaborative teams while contributing to real projects that shape the future of healthcare.
Build your future with access to the latest tools, digital innovation, and continuous learning that keep you ahead of the curve. *
Compensation
The salary range for this position is $47 - $60 hourly. All compensation will be determined commensurate with demonstrated experience.
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Salary Context
This $97K-$124K range is in the lower quartile 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 Sanofi, 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 ($111K) sits 48% below the category median. Disclosed range: $97K to $124K.
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.
Sanofi AI Hiring
Sanofi has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Morristown, NJ, US, Cambridge, MA, US, Morristown, NJ. Compensation range: $124K - $257K.
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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