Ellipsis Health is developing cutting-edge AI/ML products to address healthcare staffing issues and administrative burdens. The platform employs conversational AI and patented vocal biomarker technology to deliver improved healthcare experiences for clients and patients. The team is headquartered in Silicon Valley and is funded and supported by preeminent venture capital groups and healthcare institutions.
Responsibilities:Expand and maintain our real-time voice pipeline: design, implement, and
maintain Python micro-services for conversational AI orchestration—audio
capture, streaming transcription, prompt/LLM logic, synthesis, and playback.
Integrate new providers & transports: add plug-ins for emerging ASR, TTS,
LLM, and memory services; wire up WebRTC, SIP, or phone endpoints; build
adapters that allow hot-swapping components without downtime. Build API
endpoints.
Deliver ultra-low latency (<500 ms round-trip): profile async pipelines (asyncio,
FastAPI, gRPC), optimize buffering, concurrency, and back-pressure handling.
Instrument & observe every hop: emit structured traces (OpenTelemetry),
metrics, and logs for each pipeline stage; define SLOs for first-token latency,
end-to-end latency, and streaming reliability.
Harden for production: implement graceful retries, idempotent message
passing, circuit breakers, and HIPAA-compliant security (encryption in transit,
per-tenant isolation, secrets rotation).
Collaborate cross-functionally with ML, product, data engineering, and
client-SDK teams to deliver features such as voice cloning, multimodal hand-offs,
and domain-specific memory retrieval.
4+ years building production back-ends in modern Python.
Proven experience with real-time streaming systems—WebRTC, WebSockets,
or gRPC streaming—and proficiency with asyncio, FastAPI, or similar async
frameworks.
Deep understanding of concurrency, buffering, audio codecs (Opus, PCM), and
distributed tracing.
Solid understanding of AWS/GCP/Azure, including container orchestration
(Kubernetes/EKS/GKE), message queues (Kafka/SQS/Pub/Sub), and IaC
(Terraform).
Solid grasp of relational (PostgreSQL) and in-memory (Redis) data stores; able
to model and persist conversational state.
Excellent communication skills and a bias for measured, observable, and
continuously deployable software.
Bonus Points:B.S./M.S. in CS, EE, or related fields.
Familiarity with voice-agent frameworks,
Hands-on with telephony (Twilio, Telnyx), SIP, or PSTN integrations.
Experience integrating multimodal inputs (vision, text chat) into voice agents.
Familiarity with GPU inference and streaming pipelines.
Prior work in regulated industries (healthcare, finance) and comfort preparing for
SOC 2 / HIPAA audits.
We offer competitive salary and benefits, including 401k matching up to a certain
percentage of your salary, health, vision, and dental insurance, and very flexible paid
time off.
The typical salary range for this role is $175,000 to $250,000 USD. The amount
offered will be determined by a variety of factors including but not necessarily limited to
your individual skills, qualifications, and past experience relative to the requirements of
the role.
Background ChecksAs a health technology company, we reserve the right to run a background check on
any applicant to which we extend an offer and to re-perform any such check at any time
during the course of employment. Please know that there is no set policy on rejecting
candidates because of certain background check results, and that we look at a
candidate as a whole before making any decisions. We comply with all “ban the box”
laws in applicable jurisdictions.
AssistanceIf you have a disability or otherwise require any assistance whatsoever in the
application or recruitment process, please feel free to submit a request to
Skills Required
- 4+ years building production back-ends in modern Python.
- Proven experience with real-time streaming systems (WebRTC, WebSockets, gRPC) and proficiency with asyncio, FastAPI, or similar async frameworks.
- Deep understanding of concurrency, buffering, audio codecs (Opus, PCM), and distributed tracing.
- Experience delivering ultra-low latency systems (<500 ms round-trip) and profiling asynchronous pipelines.
- Solid understanding of AWS/GCP/Azure, including container orchestration (Kubernetes/EKS/GKE), message queues (Kafka/SQS/Pub/Sub), and IaC (Terraform).
- Solid grasp of relational (PostgreSQL) and in-memory (Redis) data stores and ability to model and persist conversational state.
- Implement production hardening: graceful retries, idempotent message passing, circuit breakers, encryption in transit, per-tenant isolation, and secrets rotation (HIPAA-compliant security).
- Emit structured traces, metrics, and logs (OpenTelemetry); define and meet SLOs for latency and streaming reliability.
- Excellent communication skills and a bias for measured, observable, continuously deployable software.
- B.S./M.S. in CS, EE, or related fields.
- Familiarity with voice-agent frameworks.
- Hands-on experience with telephony (Twilio, Telnyx), SIP, or PSTN integrations.
- Experience integrating multimodal inputs (vision, text chat) into voice agents.
- Familiarity with GPU inference and streaming pipelines.
- Prior work in regulated industries (healthcare, finance) and comfort preparing for SOC 2 / HIPAA audits.
What We Do
Ellipsis Health was founded with the belief that a person’s mental health should have the same priority as one’s physical health. The company saw an opportunity to connect the dots between the two - giving voice to a new standard of mental health care. By harnessing the unique power of the human voice as a biomarker for mental wellbeing, along with machine learning and AI, Ellipsis Health has established the first vital sign for mental health. Its technology identifies, measures, and monitors the severity of stress, anxiety, and depression at scale by analyzing a short sample of natural speech to create an objective and scalable clinical decision support tool. Through partnerships with providers, payers, employers and digital health companies, Ellipsis Health is working to positively impact the quality of care, shorten the time to diagnosis, drive workflow efficiencies, reduce costs and improve patient outcomes.







