Forward Deployed Engineer
Posted Aug 3, 2026
The role in short
Ships customer deployments of AKASA revenue-cycle AI: integrations, data onboarding and Python LLM eval frameworks across health systems.
About AKASA
AKASA is building the future of healthcare by developing AI-native solutions for some of healthcare’s most complex, consequential work. Our customers include the most innovative health systems in the country, like Cleveland Clinic, Mayo Clinic, NewYork-Presbyterian, Duke, Stanford, and Johns Hopkins. Our customer base represents 1 in 10 inpatient discharges across the United States and more than $180B+ in net patient revenue.
Our products help health systems comprehensively capture and communicate the full patient clinical journey. By empowering health systems to streamline their operations, they can focus on what matters most - delivering quality patient care.
We train internal models and build domain-specific harnesses to power our products and best capture clinical nuance for each customer. We hire people with high agency and good judgment. Everyone here is an owner and builder who is looking to help solve high stakes challenges every day.
We have raised over $250M in funding from investors such as Andreessen Horowitz, BOND, and Coatue. Our deployments have been recognized nationally as "one of the most comprehensive real-world uses of GenAI in healthcare finance to date" (link).
Some of our recent recognitions include being named one of America's Top Startup Employers 2026 by Forbes, a Top Healthcare Technology Company of 2026 by Healthcare Technology Report, the #1 most promising healthcare RCM startup of 2025 by Black Book Market Research, and we have been certified as a “Great Place to Work” for the past 6 years in a row.
About the Role
As a Forward Deployed Engineer, you’ll deliver client deployments that empower healthcare revenue cycle teams to work faster, more accurately, and with greater insight. You will work on the lifecycle and deployment of the ML models that power our products.
You’ll work closely with client engagement, product, and R&D teams to ensure smooth deployments, ongoing production stability and expansion of our deployment platform.
What You'll Do
- Execute implementation tasks for new deployments — from requirements analysis to configuration, testing, rollout, and support of production health.
- Work with client engagement teams to guide customers through technical onboarding, integration setup, troubleshooting, and data acquisition
- Build and own evaluation frameworks to continuously measure and improve LLM performance across customer deployments
- Contribute feedback and insights that shape product and platform improvements
- Work closely with R&D engineering teams to build robust scalable client solutions
Skills & Qualifications
- Bachelor's degree in Computer Science, Engineering, or similar
- 3+ years of professional Python experience
- Experience designing and running LLM evals (offline and online) to measure model quality and performance.
- Comfortable working with relational databases, APIs, and ETL pipelines
- Strong communication and interpersonal skills with meticulous attention to detail
Nice to haves
- Familiarity with FastAPI, SQLAlchemy, Postgres.
- Proficiency in Docker, Kubernetes, AWS.
- CI/CD experience with GitHub Actions.
- Experience with healthcare data exchange (FHIR, HL7, EDI)
- Frontend experience (React)
What We Offer
- Flexible paid time off (PTO)
- Expansive coverage for health, dental, and vision
- Employer contribution to Health Savings Accounts (HSA)
- Generous parental leave policy
- Full employee coverage for life insurance
- Home office stipend
- Cell phone/internet reimbursement
- Company-paid holidays
- 401(K) plan
Compensation
- Based on market data and other factors, the salary range for this position is $150,000-$170,000 + Equity. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.
The above represents the expected salary range for this job requisition. Ultimately, in determining your pay, we’ll consider your location, experience, and other job-related factors
We’re committed to doing the best work of our lives, together. Come see if we're the right team for you.
AKASA is a proud equal opportunity employer and we believe that a diverse and inclusive workforce is an imperative. We welcome people of different backgrounds, genders, races, ethnicities, abilities, sexual orientations, and perspectives, just to name a few. We do not discriminate based upon any protected class and we encourage candidates of all identities and backgrounds to apply. AKASA considers qualified applicants regardless of criminal histories in accordance with the San Francisco Fair Chance Ordinance.
AKASA is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at recruiting@akasa.com.
Prep for this interview
2 questions likely to come up at AKASA, each matched to a line in this posting.
- 1Integrations and APIs
They wrote: “Comfortable working with relational databases, APIs, and ETL pipelines”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 2LLMs, RAG and agents
They wrote: “Experience designing and running LLM evals (offline and online) to measure model quality and performance.”
How do you know an LLM feature is good enough to ship?
How to answer
Why they ask: Evals are what separate a demo from production. Many roles list them by name.
- Write 50–200 real examples with expected outcomes, with customer experts
- Score automatically where possible; review a sample by hand
- Agree on a pass bar and the errors that are never acceptable
- Re-run on every prompt or model change, and watch live traffic after launch
Tip: Describe a test set built with the customer, a pass bar agreed up front, and checks that run on every change.
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