ML Engineer

Proxima

. Build reliable scientific workflows that colleagues can run, reproduce, and troubleshoot with less manual intervention .

Posted 10/8/2026full-timeRemote • New York • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong Python programming skills and software engineering fundamentals, with a focus on building and maintaining reliable scientific workflows and ML models. Proficient in automation, infrastructure, and data management, ensuring effective communication and collaboration with researchers and engineers.

Highest-signal resume keywords

Python ProgrammingMachine Learning WorkflowsPyTorch ExperienceCI/CD AutomationKubernetes

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software Engineering FundamentalsData StructuresInterface DesignTestingConcurrencySystematic DebuggingWorkflow OrchestrationDatabase ManagementArtifact TrackingFailure Recovery

Soft Skills

Clear CommunicationStrong Sense of OwnershipProblem-SolvingIndependent ProgressCollaboration

Tools & Technologies

LinuxContainersCloud PlatformsAI Development ToolsDeveloper Tools

Industry Keywords

Scientific WorkflowsData PreparationModel ExecutionEvaluation MethodsComputational BiologyComputational Chemistry

Tech Stack

Tools & technologies

CloudKubernetesLinuxPythonPyTorch

About the role

Key responsibilities & impact

Build reliable scientific workflows that colleagues can run, reproduce, and troubleshoot with less manual intervention
Integrate new models, datasets, and evaluation methods while preserving data correctness and compatibility with existing experiments
Improve compute workflows through better job submission, monitoring, failure recovery, and artifact tracking
Identify and remove bottlenecks in data preparation, execution, and evaluation so researchers can iterate faster
Deliver libraries, services, and developer tools that are easy to use and maintain, with appropriate tests, documentation, and monitoring
Contribute to the culture of a rapidly growing company
Work closely with researchers and infrastructure engineers
Learn and work across automation, infrastructure, data, and machine learning
Help scientists develop models, run experiments, and use results
Investigate problems through deployment, including integrating datasets into training, diagnosing stalled inference jobs, building APIs for scientific results, and automating manual workflows

Requirements

What you’ll need

Strong Python and software engineering fundamentals, including data structures, interface design, testing, concurrency, and systematic debugging
Track record of delivering software that other people use and depend on, including maintaining and troubleshooting it after release
Experience building or supporting ML or scientific computing workflows, understanding how data, model execution, and evaluation fit together
PyTorch experience is required
Experience running software on Linux
Experience working with containers
Experience shipping changes through automated tests and CI/CD
Ability to break down ambiguous problems, make progress independently, and explain technical tradeoffs with evidence
Willingness to work across automation, infrastructure, data, and ML
Strong sense of ownership and clear communication with scientists and engineers
Ability to use AI development tools effectively and take responsibility for the correctness and maintainability of the resulting code
Experience with Kubernetes, cloud platforms, workflow orchestration, databases, or computational biology and chemistry is valuable
Biology or chemistry background is not required

Benefits

Comp & perks

No benefits, perks, or compensation extras are specified in the posting