1203 Senior Process Development Engineer jobs in Newark,NJ

Senior Specialist, Manufacturing Engineer

L3 Harris

Carlstadt, NJ 07072

~ 22 min OnsiteUrgently HiringBilingual PreferredEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's Degree and minimum 6 years of prior relevant experience.
Graduate Degree and a minimum of 4 years of prior related experience.
In lieu of a degree, minimum of 10 years of prior related experience.
Please be aware many of our positions require the ability to obtain a security clearance.
Security clearances may only be granted to U.S. citizens.
In addition, applicants who accept a conditional offer of employment may be subject to government security investigation(s) and must meet eligibility requirements for access to classified information.
L3Harris maintains a drug-free workplace and performs pre-employment substance abuse testing and background checks, where permitted by law.

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New, Posted 1 day ago

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Senior Vice President, Infrastructure Engineer

BNY

New York, NY 10007

$228,000/yr

New, Posted 15 hours ago

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Sr. Manufacturing Engineering Manager

Crane Company

Caldwell, NJ 07006

~ 23 min OnsiteHealth InsurancePaid Time OffRetirement Benefit

8+ years of relevant experience demonstrating expertise in manufacturing engineering and process development.
Strong understanding of continuous improvement tools, raw materials, production processes, quality control, and cost optimization techniques.
Critical thinking, time management, decision-making, and excellent communication skills; ability to motivate and develop teams effectively.
Bachelor's degree in electrical, Electro-mechanical, Industrial, or Mechanical
Eligibility Requirement: This position may require access to Controlled Data or Information. Where the position requires such access only US Persons will be considered. As a US Department of Defense contractor, we are bound by International Traffic in Arms Regulations (ITAR).
Working conditions are normal for a manufacturing environment
Manufacturing operations may require the use of safety equipment to include but not limited to: eye safety glasses, gowning, masks, hearing protectors, heel/wrist straps and any other required PPE
May be exposed to unusual environmental conditions such as loud noises, cold temperatures, confined spaces, dust or fumes
Schedule will fluctuate depending on the needs of the business
Standing: up to 100% *percentage is approximate and may vary depending on work task
Sitting: up to 100% *percentage is approximate and may vary depending on work task
Lifting (in pounds): up to 25 pounds
Pushing (in pounds): up to 25 pounds
Mental/Visual: use of computer
Workspace (line, cube, etc):work bench

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Head of AlphaGen - Investor Research Product Engineering, Director

Blackrock

New York, NY 10025

~ 30 min OnsiteUrgently HiringEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent practical experience
An advanced degree is a plus
Strategic Engineering Leadership: Proven experience leading large-scale engineering organizations through product transformation initiatives (e.g., platform modernization, cloud migration, operating model changes). Demonstrated success in setting vision and executing across complex, multi-year technology programs
Alpha generation workflows or closely related quantitative research processes in finance/investments
Cloud technologies and cloud-native architectural patterns (e.g., microservices, containerization, distributed computing e.g. Ray)
API-first platform design and developer experience best practices
AI/ML applications for workflow automation, data validation, and operational excellence
Modern stack knowledge and skills - e.g. Python, Polars, Ray, MLFlow or equivalent technology
Demonstrated strength in partnership-building and cross-functional collaboration
Able to influence and align product teams, platform/infrastructure teams, and governance bodies around a common vision and roadmap
Ability to operate in complex market and organizational contexts, translating strategic objectives into actionable plans
Proven track record of driving innovation, navigating ambiguity, and delivering measurable outcomes at scale
AI-First, Human-in-the-Loop: Embraces AI-driven solutions as a default for efficiency and scale, while keeping expert human judgment in critical loops to ensure quality and trust
Platform Over People: Prioritizes building robust platforms and automation over relying on individual heroic efforts, enabling sustainable and scalable growth
Single Front Door: Advocates for a unified user experience where researchers and stakeholders have one intuitive portal for all their needs, simplifying access and support
Standardization Everywhere: Drives consistency in tools, processes, and data across the board, reducing complexity and improving interoperability and maintainability
Self-Service as the Default: Champions self-service capabilities, empowering users to accomplish tasks independently (from data ingestion to model deployment) without bespoke engineering work
Deep Co-Ownership with PMG: Fosters a culture of partnership with the Portfolio Management Group, aligning closely on priorities, sharing roadmaps and KPIs, and co-owning outcomes to ensure the platform truly meets investment teams' needs
For New York, NY Only the salary range for this position is USD$225,000.00 - USD$285,000.00
Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits
BlackRock operates a pay-for-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance
4 days in the office per week, with the flexibility to work from home 1 day a week

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Senior Mechanical Engineer

Pts Data Center Solutions

Oakland, NJ 07436

$145,000-$190,000/yr

New, Posted 7 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Harrison, NJ 07029

~ 7 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.

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Senior Electrical Engineer

Pts Data Center Solutions

Oakland, NJ 07436

$165,000-$195,000/yr

New, Posted 15 hours ago

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Senior Manager, AI Engineering (People Leader) (Gen AI Platform Services)

Capital One

New York, NY 10001

New, Posted 7 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Weehawken, NJ 07086

~ 17 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.