Interested in this AI Software Engineer role at BIOVIA?
Apply Now →Skills & Technologies
About This Role
Roles & Responsibilities
BIOVIA, a wholly owned subsidiary of DASSAULT SYSTÈMES, provides a sophisticated enterprise system of modeling, simulation, machine learning, laboratory, quality and process execution, and data management solutions. Our customers use these systems to discover, design, and deliver innovative, sustainable, and competitive products to consumers globally. These capabilities are delivered through software applications and cloud services that empower scientists and engineers to access, understand, and analyze their data to produce quality products.
As an AI Scientific Software Engineer, you will support the development of the MARIE AI-powered companion for scientists within BIOVIA products. MARIE is a co-scientist that lives within the 3DEXPERIENCE platform—a scientific Virtual Companion that helps scientists navigate complexity with confidence.
By reasoning through, orchestrating, and executing multi-step workflows using validated scientific methods, MARIE delivers scientifically grounded, actionable outcomes. Our scientific functionality is a cornerstone of BIOVIA’s enterprise solutions for the pharmaceutical, biotechnology, and fine-chemical industries.
You will be building:
- Knowledge Integration: Connect scientific data sources and knowledge bases to MARIE so it can provide accurate, well-sourced answers that draw on both scientific literature and each customer's own data within the platform.
- Agentic AI Frameworks:
- oBuild, manage, and orchestrate agentic workflows involving tool calling, reasoning loops, memory management, skills orchestration, and multi-step task execution.
- oAI Safety & Reliability: Implement guardrails, monitoring, and validation mechanisms to ensure AI systems remain safe, reliable, and ethically grounded.
- This position will focus on developing new approaches to scientific software as part of a larger, dynamic development team dedicated to delivering high-quality, on-time software releases using best-of-breed Agile practices.
- The qualified candidate will have an excellent background in Python AI/ML programming, including AI-assisted coding.
- Familiarity with software architecture, design, optimization, and maintenance.
- In addition to being a hands-on engineer, the successful applicant will develop collaborative working relationships with application development, quality assurance, product marketing, technical communications, and technical services teams across the company.
Qualifications
- Master’s degree or above, with 3+ years of software development experience and relevant experience specifically within the AI/ML development landscape.
- AI/ML Literacy: A strong grasp of LLM fundamentals, including tokens, temperature, top-p, and context windows.
- Experience implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and vector databases.
- Prompt Engineering & Evaluation: Design, optimize, and evaluate prompts to improve AI behavior, reasoning, and response quality.
- Agentic AI Frameworks: Hands-on experience working with agentic AI frameworks such as LangChain, LangGraph, and DeepAgents, involving tool calling, reasoning loops, memory management, skills orchestration, guardrails, and multi-step task execution.
- + Experience with Model Context Protocol (MCP).
- + Familiarity with the use cases, processes, and methods used within the pharmaceutical and biotech industries, as well as by cheminformaticists and computational chemists, is a major plus.
- + Broad technical knowledge of object-oriented design and implementation, design patterns, and software development methodologies
- + Excellent written and verbal communication skills.
- + Strong analytical and organizational skills.
- + Strong personal drive to learn leading-edge technologies and excel.
- + Experience with Agile development methodologies such as Scrum.
Inclusion statement
In order to provide equal employment and advancement opportunities to all individuals, employment decisions at 3DS are based on merit, qualifications and abilities. 3DS is committed to a policy of non-discrimination and equal opportunity for all employees and qualified applicants without regard to race, color, religion, gender, sex (including pregnancy, childbirth or medical or common conditions related to pregnancy or childbirth), sexual orientation, gender identity, gender expression, marital status, familial status, national origin, ancestry, age (40 and above), disability, veteran status, military service, application for military service, genetic information, receipt of free medical care, or any other characteristic protected under applicable law. 3DS will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable state laws and local ordinances. We are committed to fair employment practices and will evaluate all candidates based on their qualifications, regardless of past arrest or conviction history. Compensation & Benefits
Dassault Systèmes offers an excellent salary with potential for bonus, commensurate with experience. Benefits include a choice of plans providing comprehensive coverage for medical, dental, vision care for employee & dependents as well as employee life, short & long term disability, tuition reimbursement, immediate 401K enrollment, 401K match (50 cents on the dollar, up to the first 8% of your eligible compensation that you contribute based on match eligibility criteria), flexible time off policy, and 10 paid holidays. Salary Pay Transparency Compensation for the role will be commensurate with experience. The total expected compensation range will be between $120000 and $145000, representing the base salary (or annualized salary based on estimated hourly compensation) and target bonus.
Salary Context
This $120K-$145K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At BIOVIA, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($132K) sits 40% below the category median. Disclosed range: $120K to $145K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
BIOVIA AI Hiring
BIOVIA has 3 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $130K - $180K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.