Machine Learning Engineer

SPECTRAFORCE; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

SPECTRAFORCE is seeking Machine Learning Engineers who thrive at the intersection of data science, modeling, and software engineering. You will design and implement models that learn from longitudinal healthcare data while balancing rigor, interpretability, and scalability.

This is an opportunity to work on foundational modeling challenges in healthcare, where your work informs clinical, actuarial, and policy decisions.

Qualifications

• Strong background in statistical modeling, ML, or data science.
• Experience with temporal/longitudinal data.
• Proficiency in Python and ML ecosystems (PyTorch/JAX/NumPyro/PyMC).
• Experience taking models from research to production.
• Software engineering skills for production ML systems.
• Ability to explain contributions and impact.
• Familiarity with probabilistic methods or Bayesian inference.
• Healthcare data experience desirable but not required.
• Experience with clinical data terminologies (ICD, CPT, SNOMED, LOINC) a plus.
• Exposure to actuarial modeling or risk-adjustment methods a plus.

Responsibilities

• Develop predictive models forecasting disease progression, utilization, and cost using temporal clinical data.
• Design interpretable ML solutions trusted by clinicians and actuaries.
• Research and prototype approaches using classical and modern ML techniques.
• Build robust, scalable ML pipelines for training, validation, and deployment.
• Collaborate with data engineers, clinicians, domain experts, and product teams.
• Conduct exploratory data analysis in partnership with domain experts.
• Communicate findings through visualizations, technical docs, and presentations.

Skills

Statistical ML

Temporal data

Python & ML libs

Production ML

Software engineering

Communication

Probabilistic methods

Healthcare domain

Clinical data codes

Actuarial exposure

Tools

Python

PyTorch

JAX

NumPyro

PyMC

Job description

***This role does NOT allow us to work with sub-vendors.***

The Role

The client is seeking Machine Learning Engineers who thrive at the intersection of data science, modeling, and software engineering. The selected individual will design and implement models that learn from longitudinal healthcare data while balancing rigor, interpretability, and scalability.

This is an opportunity to work on foundational modeling challenges in healthcare, where the individual’s work will directly inform clinical, actuarial, and policy decisions.

Responsibilities

• Develop predictive models that forecast disease progression, utilization, and cost using temporal clinical data, including claims, EHR, laboratory, and pharmacy data.
• Design interpretable and explainable ML solutions that can be trusted by clinicians, actuaries, and decision-makers.
• Research and prototype novel approaches using both classical and modern machine learning techniques.
• Build robust, scalable ML pipelines for training, validation, and deployment within distributed computing environments.
• Collaborate with data engineers, clinicians, domain experts, and product teams to align models with real-world healthcare needs.
• Conduct exploratory data analysis in partnership with domain experts.
• Clearly communicate findings and methodologies through visualizations, technical documentation, and presentations.

Requirements

• Strong background in statistical modeling, machine learning, or data science.
• Experience working with temporal or longitudinal data is preferred.
• Strong proficiency in Python and relevant ML ecosystems, such as PyTorch, JAX, NumPyro, or PyMC.
• Demonstrated experience taking models from research prototypes through production deployment.
• Strong software engineering skills and experience building production-quality ML systems.
• Ability to clearly explain individual contributions, technical decisions, and measurable impact.
• Familiarity with probabilistic methods, survival analysis, or Bayesian inference is preferred.
• Healthcare industry experience is beneficial but not required.
• Experience with clinical data or terminologies such as ICD, CPT, SNOMED CT, and LOINC is a plus.
• Exposure to actuarial modeling, claims forecasting, or risk-adjustment methodologies is a plus.

At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws.