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ewminds consulting Services
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Staff Software Engineer, Simulation
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Agility Robotics is building the world's most capable humanoid robots, and we're looking for a senior engineer to help build the simulation software at the foundation of it. You'll be one…
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WD
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Transportation Engineer
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F.H. Paschen
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Supervisory responsibility for Project Engineer(s), Assistant Project Manager(s), and Construction Intern assigned to contract(s) and/or work orders.
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Project Manager - Paschen Concrete
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Project Manager
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Supervisory responsibility for Project Engineer(s), Assistant Project Manager(s), and Construction Intern assigned to contract(s) and/or work orders
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Project Engineer II
F.H. Paschen
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As the Project Engineer, you will help the project team with all administration and documentation of the construction project while providing support to assist the team in overseeing one to…

GuruLink

Lead ML/AI Engineer

Remote, United States

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Job details

Apply by: Apr 9, 2027
Posting language: English
Posted: Nov 9, 2026

Original job description

Location: REMOTE / Nashville, Tennessee This job allows you to work remotely. Our client is a venture-backed digital health company improving specialty care for people with joint, back, and muscle (musculoskeletal) conditions. The company pulls together each patient's symptoms, medical history, preferences, and goals, combines that picture with predictive models and current clinical guidelines, and helps the patient choose the treatment most likely to work, then supports them through it.

The company is remote-first with a home base in Nashville, and is growing quickly across new markets and health plan partners. The team is collaborative, curious, and driven by the mission of getting people out of pain.

This Role:

The Lead ML/AI Engineer is the technical lead for the platform that turns model predictions into real-world actions for patients and care teams. The data science group runs a growing set of models and a real-time decision engine: predictions become events, events flow to care teams and health plan systems, and results arrive days later to be measured and fed back. Every piece has to be versioned, replayable, monitored, and cost-effective while handling protected health information.

You will set the engineering standards for the decisioning, streaming, and ML release systems, stay hands-on in the riskiest components, and hold every release to a production bar through design and code review. You will also own how the Databricks lakehouse and AWS services fit together as the platform grows. This is a senior individual contributor role reporting to the company's AI leader, working closely with data science, data engineering, software engineering, medical economics, clinical, and operations teams. The client wants someone who has built systems that choose actions and proved those actions paid off, not only models that predict.

You Will:

Own the layer between a model score and the action taken: action selection and ranking, holdout and experiment assignment, outcome capture, and the pipelines that measure lift by action and channel
Engineer the production savings (ROI) calculation so it is auditable and reproducible, working with medical economics and data science, who own the method
Design the real-time, event-driven integrations that carry model output to the systems that act on it: streaming pipelines, versioned schemas and contracts, replay, ordering, idempotency, and failure handling; own the contracts with partner teams
Set cost budgets per model and per decision for compute, streaming, inference, and LLM usage, and make cost a release criterion next to latency and accuracy
Define the production bar for models and enforce it: CI/CD, model registry, alerting, drift, coverage, and calibration by patient group, market, and health plan; build the promotion gate every model or ranking must clear before going live
Own the target architecture and roadmap for the AWS footprint alongside the Databricks lakehouse: infrastructure as code, IAM and secure data handling, containerized inference, and managed foundation-model services under PHI controls
Set technical direction for production GenAI applications, including LLM pipelines and agentic workflows, with the evaluation, retrieval, guardrails, and cost controls needed to run them safely on patient data
Own team engineering standards: code review, automated testing, reproducibility, documentation, and release discipline; lead design reviews, record architecture decisions, and mentor engineers and data scientists
Represent the team's technical position to research and development leadership, finance, and clinical and operations partners
Travel occasionally (up to roughly 10%) for onsite meetings, team sessions, and company events

Must Have Skills:

8+ years in ML engineering, MLOps, or software engineering, including 4+ years running ML systems in production
2+ years as the technical lead or architect of a production ML or decisioning system
Built or operated prescriptive systems in production: recommendation, decisioning, prioritization, or experimentation platforms that allocate a limited resource against a measured outcome
Production experience with AWS data and ML services and infrastructure as code (Terraform, CDK, or CloudFormation)
Production experience with Databricks, PySpark, Delta Lake, and MLflow, or an equivalent stack
Hands-on design and operation of event-driven or streaming systems (Kinesis, Kafka, or similar) with schema or contract management
Strong Python and SQL, with hands-on production ML work: training pipelines, model serving, feature systems, or ML platform tooling
CI/CD for ML workloads plus model and pipeline monitoring, including calibration across varied patient populations
Track record of setting engineering standards and lifting a team through code review and mentorship
Bachelor's degree in computer science, engineering, or a related field, or equivalent experience

Nice to Have Skills:

scikit-learn, XGBoost, or PyTorch modeling
Value-based care economics: savings methodology, attribution, payer contracts
Healthcare data (claims, pharmacy, eligibility, prior auth, EHR) and PHI
Uplift, bandit, or holdout-based measurement in production
dbt and lakehouse data modeling
Containerized model serving with canary deployments
Amazon Bedrock or similar under a HIPAA BAA
Production LLM apps, agents, and retrieval with evals and guardrails

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