Sales Engineer [NYC]

Concurrence•New York City, NY

1d•Onsite

About The Position

Concurrence is building a platform that enables healthcare organizations to create clinical AI agents. Its platform uses a Health World Model to unify health-system data into a patient-care record, with agents supporting workflows across the patient journey. The company reports supporting more than 10 million patient encounters and runs natively on Databricks, integrates with major EHRs, and is backed by Madrona, General Catalyst, and Optum Ventures. It has about 35 employees and works in person in New York and San Francisco. The Sales Engineer owns the technical side of a deal from the first conversation through signature, working alongside account executives and engaging with healthcare executives and their teams. The role covers pre-sales technical discovery, scoping, proof-of-concept agent development, and demonstrations that make the product tangible to prospects. Sales Engineers write code and build tailored prototypes, rather than only describing existing capabilities. They are often the sole technical person in a prospect meeting and are expected to make demonstrations robust enough to feel like a product rather than a services engagement. The team seeks complementary strengths in rapid building and healthcare-domain depth. The role will help expand coverage across customer ecosystems such as Epic, Cerner, Salesforce, and on-premise deployments. It also collaborates with Applied AI Engineers to transfer won deals into production work and shares field insights with product and engineering.

Requirements

Experience in sales engineering, solutions engineering, or a technical pre-sales role at a SaaS or AI company, with deals and personal technical contributions that can be described.
Strong hands-on coding ability and proficiency in Python; able to build a working prototype.
Experience with LLMs, prompt engineering, and building on AI platforms.
A fast, resourceful, hands-on approach to building from scratch under time pressure.
Genuine enthusiasm for AI innovation and exploring experimental use cases, with strong familiarity with developments in the field.
Ability to present to technical and executive audiences, handle objections live, and think on one's feet.
Strong discovery skills and ability to uncover underlying problems rather than only stated requirements.
Experience working on deals alongside account executives and understanding of sales cycles and buyer psychology.
Clear technical communication skills, including explaining complex systems to non-technical stakeholders without oversimplifying.
Comfort with ambiguity and incomplete prospect requirements.
Low-ego, direct working style and high personal standards.
Ability to work on site in New York City.

Nice To Haves

Experience in healthcare technology or another regulated industry.
Background as a software engineer who moved into a customer-facing role, or the reverse.
Deep knowledge of an ecosystem served by the company, such as Epic, Cerner, Salesforce, or on-premise health systems, including how those organizations buy.
Familiarity with healthcare workflows, clinical terminology, and interoperability standards such as FHIR.
Understanding of healthcare compliance requirements such as HIPAA and SOC 2.
Experience building demo environments or sandbox platforms.
Experience with voice agents and the latency and quality tradeoffs in speech-to-text and text-to-speech.
Experience building cloud partner motions, such as AWS or GCP marketplace listings and joint reference architectures.
A track record of directly influencing deal outcomes through technical work.

Responsibilities

Build rapid proof-of-concepts and demo environments tailored to prospect workflows and clinical use cases.
Partner with account executives during technical discovery to scope possibilities and shape deal architecture.
Design and deliver live technical demonstrations that translate prospect pain points into working agent experiences.
Prototype experimental agent configurations to explore new use cases and extend the platform's capabilities.
Conduct technical deep dives with prospect clinical, IT, and operations teams to understand integration requirements and workflow constraints, including unfamiliar EHRs and payor systems.
Create reusable demo assets and proof-of-concept templates to accelerate future sales cycles.
Write reference architectures, pricing and sizing models, and persona maps for cloud and data partners' field teams.
Address technical objections during the sales process, including security, compliance, and integration feasibility.
Collaborate with Applied AI Engineers to hand off won deals, documenting proof-of-concept learnings and customer expectations.
Feed prospect and market insights back to product and engineering teams, including requests, recurring patterns, and platform opportunities.
Stay current on AI and LLM developments and competitor capabilities.