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About This Role
Blue Matter is a rapidly growing management consultancy focused on the biopharmaceutical industry. We partner with clients to help them achieve commercial success across the lifecycle of their products, portfolios, and organizations.
Blue Matter is hiring for the Technology and Operations Practice within the Blue Matter Insights Team. Our Technology \& Operations practice integrates strategy, data, and AI to deliver scalable, technology\-enabled solutions for BioPharma Clients. The practice has developed a category\-defining platform called Twine™— an AI\-first customer intelligence platform purpose\-built for life sciences that is already seeing strong traction in the market. The successful candidate will play a direct role in shaping, enhancing, and scaling Twine as a core capability within our portfolio.
As an AI Engineer / Senior AI Engineer, you will work across Blue Matter as a technical expert in AI and emerging technologies, playing a key role in designing, building, and deploying AI\-powered solutions for both internal teams and clients. You will be at the forefront of integrating APIs, building proof\-of\-concepts (POCs), developing internal applications, and creating client\-facing tools and services. This role is ideal for someone who is passionate about staying current with the rapidly evolving AI landscape and eager to apply cutting\-edge techniques to solve real\-world problems in life sciences.
Primary Responsibilities
As a key member of cross\-functional client\-facing teams, develop and execute project plans to support initiatives that include, but are not limited to:
- Design, develop, and deploy AI/ML solutions and Generative AI applications to support commercial data and analytics for life sciences companies
- Integrate third\-party AI APIs (e.g., OpenAI, Anthropic, Google Gemini, Hugging Face) into internal and client\-facing applications to deliver intelligent, scalable solutions
- Build proof\-of\-concepts (POCs) and rapid prototypes to demonstrate the value of AI\-driven approaches for product launch analytics, drug discovery, patient analytics, and data operations
- Develop and maintain internal tools and applications that streamline workflows, automate operational processes, and enhance team productivity using AI
- Build client\-facing tools and services that leverage AI to deliver actionable insights, interactive dashboards, and intelligent recommendations
- Utilize cloud\-service providers (e.g., AWS, Azure, or GCP) including services such as S3, EC2, Lambda, Azure OpenAI, and SageMaker to build and deploy scalable AI solutions
- Collaborate with onshore/offshore teams to ensure timely, high\-quality deliverables, providing technical guidance and code reviews
- Design and implement data pipelines and ETL processes to support AI model training, fine\-tuning, and inference workflows
- Investigate and evaluate emerging AI frameworks, tools, and models to continuously improve the team’s AI capabilities and stay ahead of industry developments
- Contribute to Blue Matter’s Internal platform rollout and other AI\-powered product initiatives in client environments
- Address client questions, gather requirements, and deliver AI\-powered solutions aligned with business goals
- Ensure project success through effective collaboration and meeting deadlines using agile delivery model
- Stay current with the latest advancements in AI/ML, LLMs, prompt engineering, RAG, agents, and related technologies, and evangelize best practices across the organization
Desired Experience and Skills
- Hands\-on experience building AI/ML applications, including working with LLMs, Generative AI, and NLP frameworks
- Strong proficiency in Python and experience with AI/ML libraries and frameworks (e.g., LangChain, LlamaIndex, Hugging Face Transformers, PyTorch, TensorFlow)
- Proven experience integrating AI APIs (e.g., OpenAI, Anthropic, Google Vertex AI, Azure OpenAI) into production\-grade applications
- Experience building and deploying internal tools and applications (e.g., using Streamlit, Gradio, Flask, FastAPI, or React)
- Ability to build client\-facing tools and services that are reliable, user\-friendly, and production\-ready
- Expertise in building proof\-of\-concepts (POCs) and rapid prototypes to validate AI\-driven solutions
- Experienced with cloud platforms (AWS, Azure, GCP) and services
- Proficient in API development (RESTful/GraphQL) and version control (e.g., Git)
- Expertise in SQL, Python, and databases
- Familiar with data pipeline tools (e.g., Airflow, dbt) and data warehouse technologies (e.g., Snowflake, Redshift, Databricks)
- Knowledge of prompt engineering, RAG (Retrieval\-Augmented Generation), fine\-tuning, and AI agent frameworks
- Strong knowledge of AI safety, responsible AI practices, and model evaluation techniques
- Knowledge of US pharma datasets and healthcare concepts is a plus
- Strong problem\-solving, communication, and client\-facing skills
- Proficient in Agile/Scrum and project management tools (e.g., JIRA, Smartsheet)
- Genuine passion and curiosity for ongoing AI development, with a demonstrated habit of learning and experimenting with new AI tools, models, and techniques
- Excellent communication and presentation skills (internal and client facing)
Preferred Qualifications
- Advanced degree(s) (PhD, MD, MBA, or MS) in Computer Science, AI/ML, Data Science, or a related field
- 4\+ years of AI/ML engineering experience (AI Engineer) or 6\+ years (Senior AI Engineer), including client\-facing roles experience
- Demonstrated ability to integrate business and industry knowledge in developing creative AI solutions
- Active contributions to open\-source AI projects, personal AI projects, or published research in AI/ML
- Passion for Generative AI and its applications in life sciences and healthcare
Compensation:
Base salary range: $100,000 \- $150,000 per year. Generous incentive compensation structure.
Benefits Package:
- 401k \- generous employer match with immediate vesting and financial planning resources
- Comprehensive medical, dental and vision coverage options effective day 1 of employment
- Flexible spending account (FSA) or Health Savings Account (HSA)
- Company paid insurances including Short\- and Long\-Term Disability and Life insurance as well as additional voluntary options
- Paid parental leave for all new parents
- Health \& Wellness Benefit (e.g., gym memberships; $1000 reimbursement annually)
- Employee Assistance Program
- Generous paid time off including vacation, sick days, floating and company holidays
Blue Matter is an equal opportunity employer and does not discriminate against otherwise qualified applicants on the basis of race, color, religion, ancestry, age (40 and over), sex (including gender identity, sexual orientation and pregnancy), national origin, disability, marital status, parental status, genetic information, political affiliation, protected veteran status, or any other characteristic protected by law.
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Salary Context
This $100K-$150K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Blue Matter, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 25% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Blue Matter AI Hiring
Blue Matter has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span South San Francisco, CA, US, New York, NY, US. Compensation range: $150K - $225K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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