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Assistant Remote Machine Learning Engineer Jobs in Oakland, CA

Machine Learning Engineer

Mountain View, CA ยท On-site +1

$196K - $221K/yr

As a Machine Learning Engineer, you'll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systems-creating and transforming innovative research ...

... role As a Machine Learning Engineer at Elicit, you'll build products and workflows that help ... Ability to use coding assistants effectively and thoughtfully, and has adapted their workflow to ...

Sr. Lead Machine Learning Engineer

San Jose, CA ยท On-site +1

$120K - $158K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

The Opportunity We're hiring a Senior Machine Learning Engineer to join our AI team, reporting ... remote Notice of Collection and Use of Personal Information for California Residents: California ...

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

Staff Machine Learning Engineer

Mountain View, CA ยท On-site +1

$162K - $342K/yr

As a Staff Machine Learning Engineer , you will design, build, and deploy machine learning systems that power predictive analytics, personalization, automation, and intelligent platform behaviors.You ...

Showing results 21-40

Assistant Remote Machine Learning Engineer information

See Oakland, CA salary details

$37.9K

$101.9K

$154.5K

How much do assistant remote machine learning engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for assistant remote machine learning engineer in Oakland, CA is $101,932.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,000.00 and $120,000.00 per year, depending on experience, location, and employer.

What are the most commonly searched types of Remote Machine Learning Engineer jobs in Oakland, CA?

The most popular types of Remote Machine Learning Engineer jobs in Oakland, CA are:

What cities near Oakland, CA are hiring for Assistant Remote Machine Learning Engineer jobs?

Cities near Oakland, CA with the most Assistant Remote Machine Learning Engineer job openings:

Infographic showing various Assistant Remote Machine Learning Engineer job openings in Oakland, CA as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 20% Part Time, and 2% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $101,932 per year, or $49 per hour.

Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home

San Francisco, CA โ€ข Remote

Full-time

Medical, Dental, Vision, PTO

Re-posted 15 hours ago


Job description

Job Description

Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home

As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolving the most critical ML systems in the company.  The Principal Machine Learning Engineer will operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems.  While Technical Leads may own execution at the team level, you set the technical standard and shape how ML systems are built across the organization.  This is a hands-on, high-impact role focused on depth.  This position is 100% Remote.

Principal Machine Learning Engineer Responsibilities:

- Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment.

- Design reproducible, high-performance training pipelines across GPU infrastructure.

- Architect inference systems that balance latency, throughput, cost, and reliability at scale.

- Design and maintain data systems for high-quality synthetic and real-world training data.

- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.

- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.

- Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.

- Make pragmatic trade-offs and ship improvements quickly, learning from real usage.

- Work under real production constraints: latency, cost, reliability, and safety

Principal Machine Learning Engineer Outcomes:

- ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets.

- Models deployed to production achieve measurable quality improvements and meet user-impact goals.

- Production issues are proactively monitored, debugged, and resolved with clear root-cause analysis.

- Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions.

- Research-to-production cycles are efficient, safe, and continuously improve the product experience.

Qualifications

Principal Machine Learning Engineer Qualifications:

- Strong background in deep learning and transformer-based architectures.

- Artificial Intelligence (AI) experience required.

- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.

- Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.

- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).

- Strong software engineering fundamentals; you write robust, maintainable, production-grade systems.

- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.

- Comfort owning ambiguous, zero-to-one ML systems end-to-end.

- A bias toward shipping, learning fast, and improving systems through iteration.

- Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.

- Contributions to open-source ML or systems libraries.

- Background in scientific computing, compilers, or GPU kernels.

- Experience with RLHF pipelines (PPO, DPO, ORPO).

- Experience training or deploying multimodal or diffusion models.

- Experience with large-scale data processing (Apache Arrow, Spark, Ray).

Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.

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