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As we scale, our billing operations have grown more complex, spanning multiple healthcare vendors and increasingly complex workflows. We are hiring a Software Engineer dedicated to optimizing and automating Artera’s billing workflow end‐to‐end. This engineer will design, build, and continuously improve an AI-driven pipeline that orchestrates our billing processes, integrates with our healthcare vendors, and reduces manual effort across the revenue cycle. The role blends hands‐on Salesforce development, AI-model fine-tuning, vendor integration work, and modern AI‐assisted engineering to deliver durable, self‐improving billing pipeline
Design, build, and maintain automated billing workflows in Salesforce, including Apex, Flow, Lightning Web Components, and platform integrations.
Architect and operate a continuously learning Salesforce-initiated AI-driven pipeline, that takes diagnostic test information as input and routes the order through our revenue lifecycle, relying on human intervention, partner APIs, and external systems, as needed.
Translate billing requirements from Finance, Revenue Operations, and Customer Success into well‐scoped engineering deliverables with clear SLAs, observability, and rollback paths.
Fine-tune document extraction AI models with support from Arteras AI team
Use AI Code-gen and other AI‐assisted development tools to accelerate delivery, generating, reviewing, and refactoring Salesforce and Python code.
Instrument the billing pipeline with monitoring, alerting, and audit trails so that failures, anomalies, and drift are caught early and resolved without manual triage where possible.
Continuously evaluate pipeline performance and incorporate feedback loops (rules, heuristics, and lightweight ML where appropriate) so the system gets more accurate and more autonomous over time.
Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience.
2+ years of professional software engineering experience shipping and operating production systems.
Hands‐on Salesforce development experience is required, including Apex, Flow / Process Builder, Lightning Web Components, SOQL/SOSL, and Salesforce REST/SOAP APIs.
Demonstrated experience developing tools using Claude Code is required, for example, building internal automations, integration scaffolding, code generation pipelines, or developer tooling with Claude Code in a real project.
Strong proficiency in Python
Experience with version control (Git), CI/CD pipelines, automated testing, and modern Salesforce DX tooling.
Comfort designing systems for observability — logging, metrics, alerting, and end‐to‐end traceability of billing transactions.
Strong written and verbal communication skills; able to work directly with Finance, Revenue Operations, and external vendor counterparts.
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Apply to ArteraDevelop the long-term vision and roadmap for Artera’s statistical computing platform, enabling scalable and reproducible R-based workflows
Build and maintain R-based analytical environments for clinical and outcomes research
Design and operate R package infrastructure, including internal packages, dependency management, and package repositories
Build and evolve core libraries and tooling used by biostatisticians for analysis, reporting, and model validation
Partner with biostatisticians to productionize statistical methods and pipelines
Enable reproducible workflows through containerization, environment management, and versioning (e.g., renv, Docker)
Integrate statistical workflows into Artera’s broader data and AI platform ecosystem
Optimize compute, storage, and data access for large-scale clinical and real-world datasets
Ensure systems meet standards for auditability, reproducibility, and compliance
5+ years of industry experience in software engineering, data engineering, or scientific computing
3+ years of hands-on experience with R programming in production or research environments
Experience developing and maintaining R packages and shared libraries
Experience building or supporting data platforms, scientific computing environments, or analytical infrastructure
Experience with cloud platforms (AWS, GCP, or Azure)
Experience with containerization and reproducible environments (Docker, Kubernetes, etc.)
Strong proficiency in R ecosystem tools (e.g., tidyverse, renv, devtools, pak, shiny app)
Deep understanding of package management, dependency resolution, and reproducibility
Ability to design and build scalable systems for analytical workloads
Strong collaboration skills and ability to work closely with biostatistics and data science teams
Solid software engineering fundamentals (version control, testing, CI/CD)
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Apply to ArteraThe Chief Medical Officer (CMO) is a core member of the executive leadership team and the primary medical spokesperson for the company. This company leader is responsible for setting and communicating the medical vision for where the company should go by identifying the most important unmet clinical needs and ensuring that these align with business impact. The CMO will shape the company’s clinical strategy, build a strong pipeline of high-quality data and strategic collaborators, communicate with key opinion leaders, and guide the organization toward innovations that will make a measurable difference for cancer patients. The CMO must be a highly credible and influential voice within the cancer community, with trusted relationships among leading oncologists and KOLs that can accelerate adoption and shape standards of care.
This role requires a practicing or former oncologist (radiation oncologist or medical oncologist) who deeply understands what will be clinically impactful and can translate that insight into company direction. The defining quality of a strong CMO is their ability to craft a medical strategy and lead strong teams to execute the strategy.
Define and communicate a clear medical vision that aligns clinical opportunities with the company’s mission and commercial strategy.
Identify and prioritize clinical unmet needs with the greatest potential for patient benefit and market impact.
Serve as the primary medical spokesperson for the company with internal teams, investors, collaborators, and the broader oncology community.
Anticipate shifts in oncology practice, ensuring the company is positioned ahead of the curve.
Oversee strategies to build and maintain a high-quality, scalable clinical data pipeline that fuels product development, validation, and innovation.
Cultivate a strong network of academic partners, clinical collaborators, and key opinion leaders to advance the company’s scientific and commercial goals.
Ensure clinical evidence generation plans support both scientific credibility and payer adoption.
Partner closely with product, R&D, and commercial teams to integrate clinical insight into portfolio strategy.
Translate complex clinical and scientific data into actionable business decisions.
Lead teams to design, execute, and interpret clinical studies that support product development and to educate the clinical impact of the test with clinicians.
Lead team of physician leaders synthesizing collective clinical voice and expertise
Deep understanding of oncology practice, unmet needs, and pathways to adoption
Comes with a strong network of KOLs and research communities.
Experienced in developing and guiding programs spanning multiple tumor types
Visionary thinker who anticipates shifts in the clinical landscape and ensures research addresses the most impactful questions.
Skilled and persuasive communicator who inspires confidence across clinical, scientific, and business stakeholders.
Proven ability to build high-value partnerships and collaborative relationships to fuel a robust data pipeline as well as maintain strong relationships with influential groups, like guidelines committees.
Decisive leader who can cut through uncertainty, guide teams with clarity, and balance scientific rigor with business priorities.
Ensures the medical organization remains quick, nimble, and adaptable in a rapidly evolving oncology landscape
2 years of industry leadership experience
3 years of people and/or laboratory management experience
Willing to travel up to 30% per month for conferences, leadership team meetings, clinician engagements
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Apply to ArteraAs a Machine Learning Engineer at Artera, you’ll work on the AI Platform team with a focus on establishing scalable and efficient pipelines for model training, model evaluation, and data processing. You’ll work closely with AI model developers, fellow machine learning engineers, and our platform engineering team. You’ll ensure that Artera’s model developers can rely on highly efficient, large-scale training regimes and deploy optimized models to production environments.
Accountable for Artera’s ML compute infrastructure including scaling up Artera’s Foundation Model development by developing distributed training infrastructure and developer libraries.
Build and evolve the core libraries used by AI scientists to develop, launch, and monitor AI products.
Work with model developers to optimize GPU and CPU efficiency and data throughput of large-scale foundation models and downstream model training runs.
Optimize Artera’s ability to store and serve terabytes of digital pathology data efficiently for the use in serving large-scale training regimes.
Ensure that Artera’s observability infrastructure provides a clear picture of how to continue to optimize performance across our model landscape.
5+ years of industry software engineering experience
4+ years of industry experience using one of PyTorch, TensorFlow, or JAX in Python
3+ years of industry experience building with AWS, Docker, and Kubernetes
1+ years of industry experience optimizing large-scale, high data-throughput, distributed machine learning training pipelines
Experience in using ML orchestration frameworks such as Flyte, Ray, Kubeflow, Metaflow, MLFlow, Dagster, Argo Workflow or Prefect
Experience using Terraform, SqlAlchemy
Experience in multi-node and multi-gpu training.
Experience deploying and maintaining infrastructure for machine learning training and production inference
Familiarity with TorchScript, ONNXRuntime, DeepSpeed, AWS Neuron or similar approaches to inference optimization
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