New opportunity

Machine Learning Engineer

exacare ai · New York, United States

About this role

exacare ai seeks a Machine Learning Engineer focused on MLOps to move machine learning work from research into reliable, scalable, maintainable, and cost-effective production systems. The engineering-focused role supports the end-to-end ML lifecycle and works closely with researchers and other ML practitioners. The position is listed in New York, NY, with a hybrid work arrangement.

Responsibilities

Build and maintain ML workflows and infrastructure, including data pipelines, training pipelines, deployment workflows, and systems supporting model training and inference.
Partner with researchers to productionize models and enable faster iteration. Improve data processing and annotation workflows.
Develop tooling and processes to monitor model performance, system reliability, and operational health. Improve reproducibility, observability, scalability, speed, and cost-efficiency; identify bottlenecks and automate manual processes.
Help establish best practices for ML operations, deployment, and system performance.

Requirements

3+ years of experience in machine learning engineering, MLOps, ML infrastructure, data engineering, or backend/platform engineering in ML environments.
Experience supporting ML systems from model handoff through deployment and monitoring; building and owning data or training pipelines or other production ML workflows; and working with researchers, data scientists, or ML practitioners to productionize models.
Strong software engineering fundamentals, experience building production systems, and experience monitoring, debugging, and improving production ML or data systems. A track record of improving reliability, scalability, speed, or cost efficiency, and comfort with ownership in a fast-moving startup environment.

The company builds software for post-acute care. Listed benefits include competitive salary and equity, flexible PTO, medical, dental, and vision coverage, and company off-sites.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Machine learning engineeringMLOpsML infrastructureData engineeringBackend engineeringData pipelinesTraining pipelinesModel deploymentModel monitoringData processingData annotationML inferenceObservabilityAutomationDebuggingSoftware engineering

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Own production ML workflows
The role builds and maintains data pipelines, training pipelines, deployment workflows, and systems supporting training and inference. Prepare to explain how you would operate a workflow from model handoff through deployment.
Turn research into maintainable systems
The posting emphasizes partnering with researchers and ML practitioners to productionize models and enable faster iteration; it explicitly says this is not a research-first role. Prepare an example of translating a model handoff into a dependable production workflow.
Make ML systems observable
The role calls for monitoring model performance, system reliability, and operational health, alongside debugging production ML or data systems. Prepare to distinguish what you would monitor for a model from what you would monitor for its supporting infrastructure.
Improve workflow efficiency
The stated duties include improving data processing and annotation workflows, finding bottlenecks, automating manual work, and optimizing for speed, reliability, and cost. Prepare to describe how you would identify and measure a worthwhile improvement.

Likely challenges to think through

Balance iteration with reproducibility
A likely challenge is enabling researchers to iterate quickly without making training results difficult to reproduce. Prepare to discuss how you would preserve traceability while keeping the workflow usable; this is a preparation hypothesis, not a reported problem.
Trade off speed, cost, and reliability
Because the role explicitly targets all three, an optimization may improve one while worsening another. Prepare an example of choosing metrics, comparing options, and checking the production impact rather than assuming the fastest approach is best.
Prioritize broad infrastructure ownership
The remit spans pipelines, deployment, monitoring, and automation in a fast-moving startup-style environment. A likely difficulty is deciding what to improve first. Prepare to explain how you would rank work by operational risk, bottlenecks, and impact; the posting does not identify a current failing system.

Practice your answers

Technical practice

How would you take a research model from handoff to a maintained production service?

What to coverInferred preparation suggestion: Discuss how you would agree on inputs, outputs, performance expectations, and ownership with researchers, then test and deploy the model. Explain how monitoring and rollback would support reliable operation.

How would you design a data pipeline that supports repeatable ML training?

What to coverInferred preparation suggestion: Cover data validation, lineage, failure handling, and consistent inputs across runs. Explain how you would diagnose a change in data quality or pipeline output.

How would you improve a slow or error-prone annotation workflow?

What to coverInferred preparation suggestion: Describe how you would locate manual bottlenecks, check label consistency, and decide what to automate. Address the trade-off between faster processing and usable training data.

What would you build into a training pipeline to make experiments reproducible?

What to coverInferred preparation suggestion: Discuss tracking the data, configuration, code, and model artifact associated with each run. Explain how reproducibility could help researchers iterate without making production handoffs harder to verify.

How would you deploy a new model while limiting disruption to inference?

What to coverInferred preparation suggestion: Cover compatibility checks, staged evaluation, and a way to revert an unsuccessful change. Discuss the trade-off between rapid iteration and stable production behavior.

How would you tell whether a deployed model's performance is deteriorating?

What to coverInferred preparation suggestion: Distinguish model-quality signals from changes in input data and from service failures. Explain what you would measure, how you would investigate an alert, and how delayed feedback might affect evaluation.

How would you debug an intermittent failure in a production ML workflow?

What to coverInferred preparation suggestion: Describe how logs, metrics, and run context could narrow the problem to data processing, training, deployment, or inference. Explain how you would confirm the cause and check that a fix holds.

How would you find and address a scalability bottleneck in ML infrastructure?

What to coverInferred preparation suggestion: Start with measurements of throughput, latency, resource use, and failure rates. Compare possible changes by their effect on reliability, speed, and operating cost rather than optimizing one measure alone.

When would you automate a manual step in the ML lifecycle?

What to coverInferred preparation suggestion: Explain how you would assess its frequency, error risk, and effect on iteration speed. Describe how you would verify that automation improves the workflow without hiding failures.

What operational practices would you establish for a team maintaining production ML systems?

What to coverInferred preparation suggestion: Consider clear ownership, deployment checks, monitoring, and documented recovery steps. Explain which practices you would introduce first in a fast-moving team and how you would judge whether they help.

Behavioural practice

Tell me about a time you partnered with an ML practitioner to put a model into production.

What to coverInferred preparation suggestion: Use a real situation, your actions to clarify the handoff and resolve engineering constraints, and the result. Separate your contribution from the researcher's work.

Describe a production ML or data-system problem you took ownership of.

What to coverInferred preparation suggestion: State the situation and its impact, the steps you took to investigate and communicate, and the result. Include what you changed to reduce the chance of recurrence.

Give an example of balancing delivery speed with reliability or cost.

What to coverInferred preparation suggestion: Describe the competing priorities, the evidence behind your decision, and the result. Explain any trade-off you accepted rather than presenting every objective as fully optimized.

Tell me about a time you gave direct feedback or disagreed with a teammate on an engineering decision.

What to coverInferred preparation suggestion: Explain the situation, how you communicated your concern, and what happened. Focus on how you kept the discussion constructive and moved toward a decision.

Describe a time feedback from people using a system changed your engineering priorities.

What to coverInferred preparation suggestion: Use a real situation, the actions you took to understand the problem, and the result. The page emphasizes listening to operators and keeping patients downstream of decisions; if your example is from another domain, explain the relevance without claiming healthcare experience.

Sharpen your knowledge

Topics to prepare

End-to-end ML lifecycle
Review the decisions needed from researcher model handoff through deployment and monitoring. Exercise: sketch a lifecycle diagram and mark the ownership and failure points at each transition. The posting explicitly calls for end-to-end support and collaboration with researchers.
Data and training pipeline design
Review how data processing, annotation, and training stages depend on one another. Exercise: draw a pipeline with input checks, handoffs, and recovery points. The role explicitly involves building and owning these workflows.
Reproducibility and production readiness
Review what must be recorded to reproduce a training run and connect a deployed model to its inputs and outputs. Exercise: make a model-handoff checklist. The posting explicitly prioritizes reproducibility and moving research into maintainable production systems.
Monitoring and operational health
Distinguish model-performance signals from system-reliability and operational-health signals. Exercise: outline what each signal could reveal when inference quality or availability worsens. The role explicitly includes monitoring and debugging production ML systems.
Scalability, speed, and cost trade-offs
Review how pipeline and inference bottlenecks affect reliability, throughput, and cost. Exercise: compare two hypothetical improvement options and state what evidence would justify each. The posting explicitly asks for these optimizations.

Skills to revise

Build a production workflow
Practice specifying a small data-to-training workflow: inputs, processing steps, validation, failure handling, and outputs. Be ready to explain how you would maintain it; building and owning ML pipelines is an explicit requirement.
Operationalize a model handoff
Practice walking through a hypothetical researcher handoff: clarify inputs and outputs, define deployment checks, and plan how to monitor the deployed model. The posting explicitly requires working with ML practitioners from handoff through monitoring.
Diagnose a production issue
Practice tracing a hypothetical drop in model performance or inference reliability through data, training, and serving stages; state what you would inspect first and how you would verify a fix. Monitoring, debugging, and improving production systems are explicit requirements.
Remove a workflow bottleneck
Practice identifying one manual or slow step in a hypothetical annotation or training workflow, then propose an automation and a way to measure its effect. The posting explicitly calls for streamlining manual work and improving speed and efficiency.

Your preparation checklist

Select a production ML project
Prepare a walkthrough from researcher handoff through deployment and monitoring, covering your ownership, engineering decisions, and collaboration with ML practitioners. This follows the role’s stated end-to-end focus.
Diagram a pipeline you owned
Bring a simple diagram of a data or training pipeline, including inputs, validation, reproducibility controls, and failure handling. Be ready to explain any data-processing or annotation improvements you made.
Practice an MLOps design exercise
Sketch how you would move a handed-off model into a maintainable production workflow, including training, deployment, inference support, and rollback. Treat the architecture as a proposal, not as exacare ai’s existing system.
Prepare a monitoring and debugging example
Describe a production ML or data issue, how you detected and diagnosed it, what you changed, and how you checked model performance and system health afterward.
Document measurable improvements
Make a brief before-and-after summary of work that improved reliability, scalability, speed, or cost efficiency. Include metrics where available and distinguish your contribution from the team’s.
Prepare questions about the ML workflow
Ask how researchers hand off models, where data processing or annotation slows iteration, and which operational measures matter most. The posting identifies these as areas of work but does not specify the current tooling.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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