A
AI Engineer
Autonomize AI
3 hours ago
Full-time
On-site
Austin, Texas, United States
Autonomize AI is building production AI systems for
healthcare agents and copilots , where models must perform reliably on real-world workflows. This hands-on AI Engineer role focuses on delivering measurable improvements across the full lifecycle, from LLM/VML and RAG pipelines to evaluation, monitoring, and fast research-to-production execution.
The position is based in
Austin, TX
and is
onsite . You will work with a modern ML stack to ship systems that support utilization management and payment integrity, claims, and appeals.
What you’ll do
Build and optimize
production AI pipelines
that combine LLMs with classical ML, including
RAG, extraction, scoring, summarization,
and
classification
for utilization management, payment integrity, claims, and appeals.
Implement
VLM- and OCR-based pipelines
that convert medical documents, faxes, and healthcare forms into structured, reliable data.
Run
SFT
and
parameter-efficient fine-tuning
experiments (for example,
LoRA ) on both open-source and proprietary models.
Develop retrieval strategies, prompt chains, tool-using agents, and inference orchestration (for example,
LangGraph ) for production use cases.
Create
evaluation harnesses
and test sets, conduct
error analysis , and translate findings into
measurable accuracy gains .
Track and improve model quality,
latency ,
cost ,
explainability , and
safety
for models in production.
Prototype new techniques from recent research and help determine what is ready to move into production.
Collaborate with senior MLEs, product, engineering, and domain experts, while documenting work clearly.
Required qualifications
2+ years
of experience in applied ML and LLMs.
Strong
Python
skills and familiarity with
PyTorch ,
Hugging Face Transformers , and LLM frameworks such as
LangChain ,
LangGraph , and
LlamaIndex .
Comfort with
embeddings ,
vector search , retrieval pipelines, and prompt engineering.
Experience fine-tuning or adapting models, plus working knowledge of
classical ML
and
NLP .
Understanding of
model evaluation ,
observability , and
responsible AI
practices.
Experience deploying models to production, including familiarity with
MLOps tooling
such as
MLflow ,
Docker , and
Kubernetes .
Solid software engineering fundamentals with
clean, testable code .
A bias for experimentation, clarity, and shipping fast.
Experience with
healthcare ,
compliance-sensitive data , or
regulated environments .
BS/MS
in Computer Science, Engineering, Data Science, or a related field, or equivalent experience.
Technologies you’ll use
Python, PyTorch, Hugging Face Transformers
LangChain, LangGraph, LlamaIndex
Embeddings, vector search
SFT, LoRA
MLflow, Docker, Kubernetes
Benefits
Real-world impact
Category-defining AI products
Hard, unsolved ML problems
Research to production, fastSignificant ownership and autonomy
Modern stack and compute
Build your public profile
Learn with strong peers
A high-growth environment with exceptional technical challenges
Competitive compensation with performance incentives
100% employer-paid health, vision, and dental insurance
Retirement plans (401k), disability insurance, and employee assistance programs
Nice to have
Experience with
VLMs
or document AI
Exposure to healthcare payer workflows such as
UM ,
claims ,
prior authorization , and
medical coding
Experience with agent frameworks or multi-step reasoning systems
Open-source contributions, side projects, or technical writing
How you show up
Owner mentality
with a focus on learning and getting it done.
Curiosity
and an experimentation-first approach to problems.
Commitment
to the team and the mission.
Team-first collaboration and a preference for learning and winning together.
Clear communication across writing, chat, and video.
#J-18808-Ljbffr
healthcare agents and copilots , where models must perform reliably on real-world workflows. This hands-on AI Engineer role focuses on delivering measurable improvements across the full lifecycle, from LLM/VML and RAG pipelines to evaluation, monitoring, and fast research-to-production execution.
The position is based in
Austin, TX
and is
onsite . You will work with a modern ML stack to ship systems that support utilization management and payment integrity, claims, and appeals.
What you’ll do
Build and optimize
production AI pipelines
that combine LLMs with classical ML, including
RAG, extraction, scoring, summarization,
and
classification
for utilization management, payment integrity, claims, and appeals.
Implement
VLM- and OCR-based pipelines
that convert medical documents, faxes, and healthcare forms into structured, reliable data.
Run
SFT
and
parameter-efficient fine-tuning
experiments (for example,
LoRA ) on both open-source and proprietary models.
Develop retrieval strategies, prompt chains, tool-using agents, and inference orchestration (for example,
LangGraph ) for production use cases.
Create
evaluation harnesses
and test sets, conduct
error analysis , and translate findings into
measurable accuracy gains .
Track and improve model quality,
latency ,
cost ,
explainability , and
safety
for models in production.
Prototype new techniques from recent research and help determine what is ready to move into production.
Collaborate with senior MLEs, product, engineering, and domain experts, while documenting work clearly.
Required qualifications
2+ years
of experience in applied ML and LLMs.
Strong
Python
skills and familiarity with
PyTorch ,
Hugging Face Transformers , and LLM frameworks such as
LangChain ,
LangGraph , and
LlamaIndex .
Comfort with
embeddings ,
vector search , retrieval pipelines, and prompt engineering.
Experience fine-tuning or adapting models, plus working knowledge of
classical ML
and
NLP .
Understanding of
model evaluation ,
observability , and
responsible AI
practices.
Experience deploying models to production, including familiarity with
MLOps tooling
such as
MLflow ,
Docker , and
Kubernetes .
Solid software engineering fundamentals with
clean, testable code .
A bias for experimentation, clarity, and shipping fast.
Experience with
healthcare ,
compliance-sensitive data , or
regulated environments .
BS/MS
in Computer Science, Engineering, Data Science, or a related field, or equivalent experience.
Technologies you’ll use
Python, PyTorch, Hugging Face Transformers
LangChain, LangGraph, LlamaIndex
Embeddings, vector search
SFT, LoRA
MLflow, Docker, Kubernetes
Benefits
Real-world impact
Category-defining AI products
Hard, unsolved ML problems
Research to production, fastSignificant ownership and autonomy
Modern stack and compute
Build your public profile
Learn with strong peers
A high-growth environment with exceptional technical challenges
Competitive compensation with performance incentives
100% employer-paid health, vision, and dental insurance
Retirement plans (401k), disability insurance, and employee assistance programs
Nice to have
Experience with
VLMs
or document AI
Exposure to healthcare payer workflows such as
UM ,
claims ,
prior authorization , and
medical coding
Experience with agent frameworks or multi-step reasoning systems
Open-source contributions, side projects, or technical writing
How you show up
Owner mentality
with a focus on learning and getting it done.
Curiosity
and an experimentation-first approach to problems.
Commitment
to the team and the mission.
Team-first collaboration and a preference for learning and winning together.
Clear communication across writing, chat, and video.
#J-18808-Ljbffr