We Breathe Life Into Data

At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That’s why we built the Healthcare Map — the industry’s largest, most complete, precise view of the U.S. healthcare system — by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare’s most complex questions for our partners. Across the healthcare ecosystem, we’re helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease.

As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver “wow,” and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease — and enjoy the journey along the way.

The Opportunity at Komodo Health

Komodo’s Labs team builds Marmot — our AI-native product, powered by an MCP Gateway architecture that connects our data platform to the tools customers need. The Forward Deployed Engineering team is where Marmot meets the real world: we deploy AI-native solutions into complex enterprise healthcare environments, including customer cloud infrastructure, strict compliance requirements, and integrations that vary by engagement.

We sit at the intersection of Engineering, Product, and Revenue. We build, deploy, and own production outcomes for some of Komodo’s most complex and visible customer engagements

About the role

As a Senior Forward Deployed Engineer, AI Infrastructure & Deployments you will own end-to-end delivery for some of Komodo’s most technically demanding customer engagements — from solution architecture through deployment inside a customer’s cloud, data infrastructure, and compliance environment.

This is a deeply technical engineering role with direct customer context. You will write production code, design cloud-native deployment patterns, build integrations, develop MCP servers that extend the Komodo platform into a customer’s stack, and debug issues in environments you do not fully control.

The Forward Deployed Engineering team operates with a clear principle: own your work end to end. You will be expected to translate customer requirements into scalable, product-adjacent solutions, communicate clearly with technical and non-technical stakeholders, surface risks early, and make sound tradeoffs between speed and production rigor.

This role is best suited for someone who enjoys ambiguity, takes initiative, and is energized by solving complex problems in close partnership with customers. You’ll work on some of Komodo’s most complex and visible deployments — and see your work run in production.

In your first 90 days, you will have:

Learned the Marmot platform end to end, including the agent loop, MCP Gateway integration surface, and deployment toolchain that allows FDE to stand up isolated customer environments independently.
Shipped your first customer-facing deliverable, such as a new MCP server, Databricks/Delta Lake integration, or agentic workflow — and seen it run in a real customer environment.
Built strong working relationships with Sales, Solutions Engineering, and Product partners, with a clear understanding of who owns what and where you create leverage.
Mapped the technical gaps in your first customer deployment and proposed a concrete plan to close them.

By the end of your first year, you will have:

Led end-to-end delivery of at least one high-complexity customer deployment, standing up AI-native solutions inside a customer’s own cloud environment and delivering outcomes with measurable business impact.
Built and shipped production-grade agentic workflows and MCP servers that solve customer-specific problems.
Established yourself as a trusted technical partner to senior customer stakeholders by leading architecture reviews, navigating compliance constraints, and helping convert pilots into broader programs.
Contributed reusable deployment patterns to the FDE playbook, creating frameworks the team can rely on for future customer engagements.
Participated in Komodo’s architecture governance, influencing platform decisions based on what you have seen and built in the field.
Partnered cross-functionally with Sales, Product, Data, and Core Engineering to close gaps and shape the roadmap.

These are the essential job duties you will be responsible for …

Design, build, and deploy AI-native solutions inside customer environments, including MCP servers, agentic workflows, and custom integrations that adapt Komodo’s platform to each customer’s cloud infrastructure, systems, data contracts, and compliance requirements.
Own the infrastructure layer for customer deployments, including Terraform-managed AWS environments, VPC networking, IAM, security controls, and data isolation requirements.
Work with data at scale across tools like Databricks, Delta Lake, Snowflake, and S3 — debugging pipeline failures, optimizing performance, and building integrations that hold up in production.
Drive day-to-day technical engagement with customer stakeholders by scoping work, communicating progress, explaining tradeoffs, surfacing risks early, and building trust over time.
Turn field learnings into reusable FDE patterns, deployment templates, engineering standards, and roadmap input for Core Platform and architecture governance.

What you bring to Komodo Health (required):

We know strong candidates may not have every tool listed below. We’re most interested in engineers who have shipped production systems, learn quickly, and can operate across AI, infrastructure, data, and customer-facing technical work.

Production engineering experience: 7+ years of software engineering experience, with a track record of building and owning systems that run in production.
Agentic AI experience: Hands-on experience building LLM-powered applications, agentic workflows, or AI tools beyond prototypes. Experience with frameworks such as LangGraph, Strands, CrewAI, or similar is a plus.
Cloud infrastructure ownership: Strong AWS and Terraform experience, including VPC networking, IAM, security controls, and standing up reliable customer or single-tenant environments.
Application engineering depth: Strong Python skills, experience with APIs, async service patterns, and building software that other teams, customers, or businesses depend on.
Data platform experience: Experience working with large-scale data systems such as Databricks, Delta Lake, Snowflake, S3, or similar platforms, including debugging failures and improving performance or reliability.
Security and enterprise judgment: Comfort designing for data isolation, customer security requirements, and regulated environments with constraints such as SOC 2, BAA, HIPAA, or similar considerations.
Integration mindset: Experience building integration surfaces for AI systems, including MCP servers or equivalent patterns that connect tools, data, and workflows.
Customer-facing technical leadership: Ability to lead technical conversations with customer engineers and senior stakeholders, explain tradeoffs clearly, drive alignment, and build trust without handing off the conversation.
Comfort with ambiguity: Ability to work across infrastructure, application, data, and AI layers, find a path when one does not already exist, and deliver on what you commit to.

Nice to have:

Experience with LLM evaluation frameworks in production, such as LangSmith, Braintrust, Ragas, or equivalent tools.
Familiarity with healthcare or life sciences data, including IQVIA data structures, pharma customer workflows, payer data contracts, or similar data environments.
Experience with API gateway or service mesh patterns used for AI tool integration.
Contributions to shared platform infrastructure, including code committed to shared repos, participation in architecture reviews, or tooling that other engineers depend on.
Experience deploying in regulated environments with data residency, audit logging, or multi-party data access constraints.

A Note On Travel:

This role does not require regular travel. Some high-complexity deployments may benefit from occasional on-site customer engagement to build trust, align stakeholders, and strengthen long-term partnerships.

Open to remote or hybrid in NYC/SF

#LI-Remote

The pay range for each job posting reflects a minimum and maximum range of annual base pay that we reasonably expect to pay for this position within the US. We carefully consider multiple business-related factors when determining compensation, including job-related skills, work experience, geographic work location, relevant training and certifications, business needs and market demands.

The starting annual base pay for this role is listed below. This position may be eligible for performance-based bonuses as determined in the Company’s sole discretion and in accordance with a written agreement or plan. This role may also be eligible for equity awards. In addition, this role is eligible for benefits including, but not limited to, comprehensive health, dental, and vision insurance; flexible time off and holidays; 401(k) with company match; disability insurance and life insurance; and leaves of absence in accordance with applicable state and local laws and regulations and company policy.

San Francisco Bay Area and New York City:

$220,000—$253,000 USD

All Other US Locations:

$191,000—$220,000 USD

Komodo's AI Standard

At Komodo, we're not just witnessing the AI revolution – we're leading it. This is a pivotal moment in time, where being first to market with AI transforms industries and sets the bar. We've already established industry leadership in leveraging AI to revolutionize healthcare, and we expect every team member to contribute. AI here isn't optional; it's foundational. We expect you to integrate AI into your daily work – from summarizing documents to automating workflows and uncovering insights. This isn't just about efficiency; it's about making every moment more meaningful, building on trust in AI, and driving our collective success.

Join us in shaping the future of healthcare intelligence.

Where You’ll Work

Komodo Health has a hybrid work model with hubs in San Francisco, New York City, and Chicago. Roles vary — some can be performed from anywhere in the country, others are scoped to a specific region, and some are based near one of our hubs. For hub-based Dragons, we're building intentional in-office rhythms alongside the flexibility that's core to how we work. Whatever your setup, expectations will always be clear before you join.

Equal Opportunity Statement

Komodo Health provides equal employment opportunities to all applicants and employees. We prohibit discrimination and harassment of any type with regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

By submitting your application, you acknowledge that you have read and understand Komodo Health’s Privacy Notice for Employees and Contractors.

This notice explains how we collect, use, and retain applicant data.

Applied Ai Forward Deployed Machine Learning Engineer Montreal Jobs

Find Your Next Role

6,419 Open Positions

Applied Ai Forward Deployed Machine Learning Engineer Montreal Salary Ranges

Companies Hiring for Applied Ai Forward Deployed Machine Learning Engineer Montreal Roles

Where Applied Ai Forward Deployed Machine Learning Engineer Montreal Jobs Are Located

Common Applied Ai Forward Deployed Machine Learning Engineer Montreal Job Titles

Applied Ai Forward Deployed Machine Learning Engineer Montreal Salary Ranges1876

Companies Hiring for Applied Ai Forward Deployed Machine Learning Engineer Montreal Roles8

Where Applied Ai Forward Deployed Machine Learning Engineer Montreal Jobs Are Located6

Common Applied Ai Forward Deployed Machine Learning Engineer Montreal Job Titles8