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About This Role
Opportunity Overview:
Reporting to the Chief Digital \& Technology Officer, the Vice President of AI Engineering \& Agent Platforms will lead the teams responsible for AI platform engineering, agent platforms, agent runtime systems, skills and prompt lifecycle management and framework, AI infrastructure, MLOps/LLMOps, and forward deployed AI engineering.
This role partners closely with the Chief Data \& AI Officer, who owns Cohere's AI strategy, model development, evaluation frameworks, prompt design and governance, skills requirements and behavior, knowledge management frameworks, and data science functions. The VP of AI Engineering \& Agent Platforms is responsible for operationalizing, scaling, deploying, and running those capabilities across Cohere's products and customer environments.
This leader will build the platforms, engineering systems, and deployment capabilities that enable Cohere to rapidly deliver AI\-powered solutions while maintaining the reliability, security, and compliance required in healthcare.
What You'll Do:
-------------------
### Build and Scale Our AI Platform
Lead the engineering organization responsible for the foundational platforms and services that power Cohere's AI ecosystem.
Responsibilities include:
- AI infrastructure and runtime platforms
- Agent orchestration, workflow, and execution services
- Document processing and knowledge ingestion pipelines
- MLOps and LLMOps capabilities
- AI observability, monitoring, and reliability
- Partnership with core teams to build AI native Developer platforms and engineering productivity tools
- Build and evolve Cohere's enterprise agent platform, enabling teams to rapidly develop, evaluate, deploy, govern, and operate AI agents at scale.
### Lead Agent Engineering
Build the frameworks, services, and reusable capabilities that enable teams to rapidly develop, test, deploy, and operate secure, observable, and production\-ready AI\-powered solutions.
Areas of focus include:
- Agent architectures, orchestration, and runtime frameworks
- Multi\-agent systems and workflow automation
- Skills management and reusable action frameworks
- Evaluation, testing, and agent observability infrastructure
- Human\-in\-the\-loop and supervised AI workflows
- Enterprise integrations and action surfaces
- Partnership in skills design with data science
Design and scale the engineering systems used to build, manage, deploy, and govern reusable agent skills across healthcare workflows.
### Lead Prompt and Skills Lifecycle Operations
Establish the platforms and operational capabilities required to manage AI behavior at scale.
Responsibilities include:
- Prompt lifecycle management
- Prompt deployment and versioning
- Prompt testing infrastructure
- Skills deployment and governance
- Agent configuration management
- AI release management and rollback capabilities
### Scale a Forward Deployed AI Engineering Organization
Lead a team of customer\-facing engineers responsible for deploying and operationalizing Cohere's AI solutions within customer environments.
This organization partners closely with customers to:
- Implement AI\-powered workflows
- Integrate with enterprise systems
- Accelerate adoption and value realization
- Establish repeatable deployment patterns that enable scale
- Support complex customer implementations and transformations
### Drive Operational Excellence
Establish engineering best practices, platform standards, and operational processes that allow Cohere to scale AI safely and efficiently across customers, products, and healthcare workflows.
Partner closely with Product, Clinical Operations, Customer Success, Security, and the Chief Data \& AI Officer's organization to ensure AI capabilities move efficiently from concept to production.
What you'll need:
---------------------
Must\-Haves
- 15\+ years of software engineering experience, including significant leadership responsibility
- Experience leading large\-scale platform, infrastructure, or AI engineering organizations
- Proven track record building and operating cloud\-native, data\-rich products and platforms
- Experience deploying AI\-powered applications into production environments
- Experience with AWS or other modern cloud\-native technologies
- Healthcare or other highly regulated industry experience
- Deep understanding of distributed systems, platform engineering, and modern software architecture
- Experience building and leading high\-performing engineering teams
### Nice\-to\-Haves
- Experience with generative AI, agentic systems, and AI platform development
- Experience building agent platforms, skills frameworks, or AI developer platforms
- Experience with MLOps, LLMOps, AI infrastructure, and developer tooling
- Experience working directly with enterprise customers on complex technical implementations
- Track record developing data\-rich applications leveraging structured and unstructured data
- Experience leading customer\-facing engineering or forward deployed engineering organizations
Leadership Characteristics
------------------------------
The ideal candidate is:
- A platform builder who thinks in systems, scale, and reusable capabilities
- Passionate about turning innovation into reliable, production\-ready products
- Customer\-focused and outcome\-oriented
- Comfortable leading through rapid growth and organizational change
- Equally effective in technical architecture reviews and executive discussions
- Excited about helping define the future of agentic AI in healthcare
This is a remote\-first role that may require travel to Boston, MA for new hire onboarding and occasional in\-person team meetings and company events.
Pay \& Perks:
Fully remote opportunity with about 15% travel
Medical, dental, vision, life, disability insurance, and Employee Assistance Program
401K retirement plan with company match; flexible spending and health savings account
️ Flex Time Off \+ company holidays
Up to 14 weeks of paid parental leave
Pet insurance
The salary range for this position is $280,000 to $340,000 annually; as part of a total benefits package which includes health insurance, 401k and bonus. In accordance with state applicable laws, Cohere is required to provide a reasonable estimate of the compensation range for this role. Individual pay decisions are ultimately based on a number of factors, including but not limited to qualifications for the role, experience level, skillset, and internal alignment.
Interview Process\*:
- Connect with Talent Acquisition for a Preliminary Phone Screening
- Meet your Hiring Manager!
- Behavioral Interview(s)
- Subject to change
About Cohere Health:
Cohere Health's clinical intelligence platform and agentic AI\-powered solutions connect health plans' strategic goals and providers' needs, optimizing the speed, cost, and quality of care. With an enterprise approach that streamlines payer\-provider decision\-making across the care continuum–including policy, prior authorization, payment accuracy, and more–the company improves collaboration and reduces burden, resulting in up to 8x ROI and 94% provider satisfaction.
With the acquisition of ZignaAI, we've further enhanced our platform by launching our Payment Integrity Suite, anchored by Cohere Validate™, an AI\-driven clinical and coding validation solution that operates in near real\-time. By unifying pre\-service authorization data with post\-service claims validation, we're creating a transparent healthcare ecosystem that reduces waste, improves payer\-provider collaboration and patient outcomes, and ensures providers are paid promptly and accurately.
Cohere Health's innovations continue to receive industry wide recognition. We've been named to the 2025 Inc. 5000 list and in the Gartner® Hype Cycle™ for U.S. Healthcare Payers (2022\-2025\), and ranked as a Top 5 LinkedIn™ Startup for 2023 \& 2024\. Backed by leading investors such as Deerfield Management, Define Ventures, Flare Capital Partners, Longitude Capital, and Polaris Partners.
The Coherenauts, as we call ourselves, who succeed here are empathetic teammates who are candid, kind, caring, and embody our core values and principles. We believe that diverse, inclusive teams make the most impactful work. Cohere is deeply invested in ensuring that we have a supportive, growth\-oriented environment that works for everyone.
We can't wait to learn more about you and meet you at Cohere Health!
Equal Opportunity Statement:
Cohere Health is an Equal Opportunity Employer. We are committed to fostering an environment of mutual respect where equal employment opportunities are available to all. To us, it's personal.
\#LI\-Remote
\#BI\-Remote
Salary Context
This $280K-$340K range is above the 75th percentile 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 Cohere Health, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($310K) sits 42% above the category median. Disclosed range: $280K to $340K.
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.
Cohere Health AI Hiring
Cohere Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $340K - $340K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below 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 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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