1085 Audio Visual Av Engineer jobs in New York,NY

High school graduate
3-5 Years mechanical inspection, manufacturing experience
Experience in basic and advanced inspection techniques using various inspection tools and methodologies
Ability to read and interpret engineering drawings using geometric dimensioning and tolerances including and understanding of maximum material conditions
Strong understanding of GD&T;
Inspect detail parts per drawing requirement and specs using industry-standard measuring equipment
Prepare and maintain inspection reports
Program and/or operate Coordinate Measuring Machines
Perform small hand tool calibration
Collect and tabulate data for SPC use

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Personal Trainer, Park Ave

Equinox

New York, NY 10261

$17/hr

OnsiteEducation AssistanceHealth InsuranceRetirement Benefit

Current Personal Training certification or willingness to obtain one through certification reimbursement program.
Eligibility to begin a higher Tier & pay rate based on previous experience and education (Degree in the field)
Current CPR/AED certification
Ability to work in-person during weekdays/weekends
Basic computer and technology skills
Must have a legal right to work in the United States.

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New, Posted 16 hours ago

DiversityJobs

Senior PCB Layout Engineer

CIENA

New Providence, NJ 07974

Flexible Schedule

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Principal Engineer - Python API Development

Fidelity Investments

Jersey City, NJ 07311

~ 22 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.