656 Security Reliability Engineer jobs in New York,NY

Senior Vice President, Infrastructure Engineer

BNY

New York, NY 10007

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iHire

Senior Distinguished Engineer, AI Compute (Remote Eligible)

Capital One

New York, NY 10001

Remote

New, Posted 21 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Weehawken, NJ 07086

~ 12 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

SmartExplore AI is experimental.

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Electronic Warfare, Systems Engineering Lead

L3 Harris

Clifton, NJ 07011

~ 30 min OnsiteBilingual PreferredEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's Degree and minimum 9 years of prior relevant experience.
Graduate Degree and a minimum of 7 years of prior related experience.
In lieu of a degree, minimum of 13 years of prior related experience.
9 years of experience in systems engineering for radar, RF, or sensor systems
Ability to obtain secret clearance
Travel as needed to support program execution and customer engagement, including periodic travel to Amityville, NY - 25%
10%-20% travel
Background checks
Pre-employment substance abuse testing

SmartExplore AI is experimental.

New, Posted 20 hours ago

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ZipRecruiter

Staff Backend Engineer, Customer Value Optimisation

HelloFresh

New York, NY 10025

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Fleet Technician II, Journeyman - Hiring Immediately

SYSCO

Jersey City, NJ 07308

$24.86-$37.26/hr

~ 22 min OnsiteUrgently Hiring

High school diploma or equivalent.
Medium/Heavy Duty truck technical training 2-year school.
Either, 1 Year Experience with 2 Year Technical Degree or 3 years experience in Fleet Maintenance and Repair.
ASE Certification ' Automotive or Medium/Heavy Duty.
Current and Valid Drivers License, CDL-A Preferred.
Annual Inspector Certification Knowledge Skills and Abilities.
Brake Inspector Certification Knowledge Skills and Abilities.
Basic Knowledge and skill of all vehicle systems and Components.
Knowledge and skill in the diagnosis and repair of electrical systems on all equipment.
Understanding and skill in welding and cutting (gas, electric, MIG, TIG).
Working knowledge of Computer-based diagnostic software for OEM component mfg

SmartExplore AI is experimental.

ResumeLibrary

Senior AWS Redshift Data Engineer (Contract | 1099)

BrainTrust

New York, NY 10001

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Digital Pathology Field Service Engineer

TD Synnex

New York, NY

Flexible Schedule

New, Posted 21 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

North Bergen, NJ 07047

~ 18 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.