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Hire Train Deploy Developer Jobs in California (NOW HIRING)

RainesDev

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

... research, prototype, train, deploy, and iterate rapidly. * Build multimodal ML systems for ... Opportunity to be an early ML hire in a strong engineering-first culture. * High autonomy and ...

Replit

Head of Forward Deployed Engineering

Foster City, CA · On-site

$300K - $400K/yr

Hire and develop a high-talent-density team of Forward Deployed Engineers * Define the hiring bar, operating principles, and execution standards for the organization * Build a team culture centered ...

Saga

Senior AI Engineer

Los Altos, CA · On-site +1

$123K - $169K/yr

The Role As a Senior AI Agent Engineer, you'll be at the center of how we build, train, deploy, and operate character AI agents at scale. You'll work across the full lifecycle - from fine-tuning ...

Saga

Senior AI Engineer

Los Altos, CA · On-site +1

$123K - $169K/yr

The Role As a Senior AI Agent Engineer, you'll be at the center of how we build, train, deploy, and operate character AI agents at scale. You'll work across the full lifecycle - from fine-tuning ...

Nace AI

Senior MLOps Engineer

Palo Alto, CA · On-site

$122K - $168K/yr

As a Senior MLOps Engineer, you will own the infrastructure that takes Nace.AI's models from ... train, deploy, monitor, and continuously improve many specialized models at once, with the ...

Intuit

Senior Staff AI/MLE Scientist

Mountain View, CA · On-site

$210K - $284K/yr

... engineering hygiene, training reproducibility, deployment patterns, and post-launch monitoring. Train, deploy, and maintain batch models that power targeting, retention, and personalization ...

Intuit

Senior Staff AI/MLE Scientist

Mountain View, CA · On-site

$210K - $284K/yr

... feature engineering hygiene, training reproducibility, deployment patterns, and post-launch monitoring. • Train, deploy, and maintain batch models that power targeting, retention, and ...

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Hire Train Deploy Developer information

What is the difference between Hire Train Deploy Developer vs Software Developer?

AspectHire Train Deploy DeveloperSoftware Developer
CredentialsTypically requires basic programming knowledge, often with certifications in specific technologiesUsually holds a degree in Computer Science or related field, with strong coding skills
Work EnvironmentOften in client sites or training centers, focusing on skill development and deploymentPrimarily in office or remote settings, developing software solutions
Employer & Industry UsageUsed by staffing agencies and companies for rapid deployment of trained developersHired directly by companies for ongoing software development projects

In summary, a Hire Train Deploy Developer is trained and prepared for immediate deployment in specific projects, often with a focus on skill readiness, while a Software Developer typically has a broader, formal education background and works on developing software solutions over longer periods.

What are Hire Train Deploy Developers?

Hire Train Deploy Developers are professionals who are recruited and trained by specialized companies to meet specific client requirements before being placed on client projects. This model allows organizations to quickly fill skill gaps with candidates who have received tailored technical and soft skills training. The process ensures that developers are ready to contribute effectively from day one, reducing onboarding time and increasing project success rates.

What can I expect from the training phase as a Hire Train Deploy Developer, and how does it prepare me for client projects?

As a Hire Train Deploy Developer, the training phase is an intensive period where you’ll gain hands-on experience with the technologies and methodologies commonly used by client organizations. This training is designed to bridge any skill gaps, ensuring you're well-versed in relevant frameworks, tools, and best practices. You’ll often work on simulated client scenarios and collaborate with other trainees, fostering teamwork and communication skills that are vital on real projects. This approach not only boosts your technical proficiency but also acclimates you to the types of challenges and workflows you’ll encounter once deployed to a client site.

What are the key skills and qualifications needed to thrive as a Hire Train Deploy Developer, and why are they important?

To thrive as a Hire Train Deploy Developer, you need a strong grasp of programming fundamentals, problem-solving abilities, and typically a relevant degree or equivalent coding bootcamp experience. Familiarity with industry-standard tools such as Git, cloud platforms, and agile project management systems, as well as any required certifications, is often expected. Effective communication, adaptability, and a willingness to learn are crucial soft skills, especially when transitioning between client projects or technologies. These skills ensure that you can quickly integrate into client teams, deliver value, and continuously upskill in a dynamic deployment model.
What job categories do people searching Hire Train Deploy Developer jobs in California look for? The top searched job categories for Hire Train Deploy Developer jobs in California are:
What cities in California are hiring for Hire Train Deploy Developer jobs? Cities in California with the most Hire Train Deploy Developer job openings:
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Staff Machine Learning Engineer, Tech Lead, Labeling Automation

Staff Machine Learning Engineer, Tech Lead, Labeling Automation

Waymo

Mountain View, CA • Hybrid

Other

Posted 5 days ago

Job description

As a Staff Software Engineer (L6) on the Labeling Team, you will lead the technical strategy for automating our data pipelines. You will build cutting-edge auto-labeling systems to drastically scale our throughput and develop intelligent auto-graders to guarantee exceptional data quality. This is a high-impact leadership role where you will train, deploy, and orchestrate state-of-the-art computer vision architectures and Vision-Language Models (VLMs) to solve complex semantic labeling challenges across our massive autonomous driving fleet.

In this hybrid role, you will report to a Technical lead Manager, Staff Software Engineer.

You will:

  • Architect and Scale Auto-Labeling: Lead the design and deployment of highly scalable auto-labeling pipelines that significantly improve data throughput and reduce our reliance on manual annotation bottlenecks.
  • Build Robust Auto-Graders: Develop automated anomaly detection and quality evaluation systems (auto-graders) to assess annotation accuracy, detect regressions, and enforce rigorous quality standards across millions of labels.
  • Train & Deploy SOTA Computer Vision Models: Train, optimize, and push into production advanced 2D and 3D computer vision models. You will utilize architectures ranging from foundational zero-shot models like SAM (Segment Anything Model) and efficient real-time detectors like YOLO, to bespoke 3D perception and tracking models.
  • Leverage Vision-Language Models (VLMs): Fine-tune, and deploy large VLMs and LLMs, utilizing prompt optimization and advanced post-training techniques (SFT, RL, etc.), to solve complex, open-set labeling and contextual reasoning tasks.
  • Drive Technical Direction: Act as a technical pillar for the Labeling organization. Set the long-term ML strategy, guide architectural decisions, and mentor senior and mid-level engineers.
  • Collaborate Cross-Functionally: Work closely with Perception, Planner, and Simulation teams to align labeling capabilities with the evolving ML data needs of the Waymo Driver.

You have:

  • 8+ years of professional experience in the field of software engineering and applied machine learning
  • Experience programming in C++ or Python
  • Experience building, evaluating, and deploying deep learning models for object detection, segmentation, and spatial tracking
  • Experience in large model training, distributed computing, and scaling deep learning architectures using frameworks like PyTorch or TensorFlow
  • Experience taking machine learning solutions through the entire lifecycle-from research and experimentation to robust, scaled production deployment

We prefer:

  • Experience building internal tooling for ML developers

#LI-hybrid

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