Top 43 Ai algorithms transformer architecture Jobs in San Francisco, Ca

AI Engineers
Accellor —San Francisco, CA
- Research
- Linux
- Senior level
Quick Apply
AI Researcher (Computer Vision/Multimodal/Generative AI)
SpreeAI —San Francisco, CA
- Computer Science
- Doctor of Philosophy
- Senior level
AI Engineer, LLMs
Logical Intelligence —San Francisco, CA
- Mid-level
- 3 years
AI/ML Infrastructure Engineer
Zensors —San Francisco, CA
- Full-time
- Computer Science
- Bachelor of Science
- SQL
Quick Apply
Robotic AI Engineer/Applied Scientist - Foundation Models
Maven Robotics —San Francisco Bay Area, CA
- Computer Science
- Doctor of Philosophy
- Senior level
Machine Learning Engineer (Computer Vision/Multimodal/Generative AI)
SpreeAI —San Francisco, CA
- Computer science
- Computer Science
- Mid-level
AI Researcher, Core ML (Turbo)
Together AI —San Francisco, CA
- $200,000 - $280,000 a year
- Full-time
- Computer science
- Computer Science
- System design
- Health insurance
AI Researcher / ML Engineer (ASR & Speech Specialist)
LILT —San Francisco, CA
- Full-time
- Remote
- Computer Science
- 3 years
- Master's degree
Fellow Software Engineer-Eng (Agentic AI Architecture & Engineering)
UKG (Ultimate Kronos Group) —San Francisco, CA
- $233,300 - $385,000 a year
- Remote
- Azure
- Computer Science
- 5 years
Lead, AI Engineering
Bain & Co. —San Francisco, CA
- $179,500 - $203,500 a year
- Full-time
- Azure
- Office
- 3 years
- Wellness program
- Paid holidays
Quick Apply
Application Engineer – Computer Vision
EchoTwin AI —San Francisco, CA
- Full-time
- Computer Science
- System design
- Mid-level
- Profit sharing
- Health insurance
Machine Learning Engineer
DeepMotion —San Francisco Bay Area, CA
- Email
- Azure
- Computer Science
- Bachelor of Science
AI Senior Scientist
Vivodyne —San Francisco, CA
- $220,000 - $270,000 a year
- Computer science
- Computer Science
- System design
Research Scientist, Interpretability
Anthropic —San Francisco, CA
- $350,000 - $850,000 a year
- Hybrid work
- Office
- Bachelor's degree
- Experience equivalent to degree accepted
- Parental leave
- Flexible schedule
Machine Learning Engineer, Integrity
OpenAI —San Francisco, CA
- $266,000 - $555,000 a year
- Full-time
- Computer Science
- Master's degree
- Analysis skills
Machine Learning Infrastructure Engineer
Zensors —San Francisco, CA
- Full-time
- Computer Science
- Bachelor of Science
- SQL
SDE- ML Engineer, Frontier AI Robotics
Amazon.com Services LLC —San Francisco, CA
- $129,300 - $223,600 a year
- Full-time
- Computer science
- Computer Science
- Mid-level
Senior Research Scientist
Sciforium —San Francisco, CA
- $190,000 - $250,000 a year
- Full-time
- Computer Science
- 5 years
- Doctor of Philosophy
- Health insurance
- Dental insurance
AI Inference Engineer
quadric, Inc —Burlingame, CA
- $110,000 - $270,000 a year
- Full-time
- Hybrid work
- Computer Science
- 5 years
- Office
- Health insurance
- Dental insurance
Quick Apply
Research Engineer, Core ML
Together AI —San Francisco, CA
- $200,000 - $280,000 a year
- Full-time
- Computer science
- Computer Science
- System design
- Health insurance

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AI Engineers

Accellor
San Francisco, CA

Quick Apply

Job Details

Qualifications

GPU programming
GPU architecture
Data structures
Git
Research
AI platforms (beyond public GPTs)
Computational framework
Docker
CNN
Developing large-scale AI models
Model training
Linux
Neural networks
Technical troubleshooting support
Multithreading
Model evaluation
Machine learning frameworks
Shell Scripting
Debugging

Full Job Description

Accellor is an AI-native services firm purpose-built for the post-ChatGPT era. Free from legacy constraints, we focus on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. Our mission is to operationalize AI at scale and unlock sustained enterprise value.

Our offerings span AI solutions, data services, enterprise applications, and product engineering, tailored to industry-specific needs across healthcare, life sciences, telecom, retail, financial services, and technology. By leveraging design thinking and technology-agnostic architectures, we ensure faster time-to-value and seamless interoperability.

With a proven track record of enabling Fortune 100 enterprises and global innovators, Accellor stands as a trusted partner for organizations seeking to harness the full potential of AI. Our vision is clear: to build intelligent, connected ecosystems that deliver measurable outcomes and redefine the future of enterprise transformation.

Role Summary:

We are looking for an AI Engineer with strong problem-solving ability, solid Python engineering skills, and hands-on understanding of machine learning, deep learning, GPUs, and CUDA fundamentals. The ideal candidate may be early in their career but must be technically sharp, curious, implementation-driven, and capable of learning complex AI systems quickly.

This role is suited for someone who can build models, debug training issues, optimize GPU workloads, understand tensor operations, and work closely with research, platform, and engineering teams.

Key Responsibilities:

Design, build, train, and evaluate AI/ML models using Python, TensorFlow, PyTorch, or JAX.
Develop clean training pipelines with data loading, checkpointing, logging, validation, and experiment tracking.
Work on deep learning models including CNNs, Transformers, LLMs, embeddings, and attention-based architectures.
Debug model issues such as poor convergence, overfitting, unstable loss, NaNs, tensor-shape errors, and GPU memory failures.
Run and optimize models on GPUs with awareness of CUDA execution, memory usage, batching, mixed precision, and kernel performance.
Profile training and inference workloads to identify bottlenecks in compute, memory, data loading, and communication.
Support development of custom operators, fused kernels, CUDA/Triton-based optimizations, or framework-level performance improvements.
Build AI inference pipelines with focus on latency, throughput, reliability, cost, and quality.
Create evaluation pipelines for model accuracy, robustness, hallucination risk, safety, latency, and regression testing.
Read research papers, implement ideas, run experiments, and clearly document findings, trade-offs, and limitations.

Requirements

Strong programming experience in Python.
Good understanding of data structures, algorithms, numerical programming, and clean code practices.
Hands-on experience with TensorFlow, PyTorch, or JAX.
Strong fundamentals in:

o Deep learning

o Backpropagation

o Gradient descent

o Optimizers

o Loss functions

o Regularization

o CNNs

o Transformers

o Attention mechanisms

o Embeddings

o Model evaluation

Experience training and debugging models on CUDA-enabled GPUs.
Basic understanding of:

o GPU memory

o CUDA kernels

o Threads, blocks, and warps

o Memory bandwidth

o Kernel launch overhead

o Mixed precision training

o CUDA out-of-memory debugging

Comfortable with Linux, Git, Docker, shell scripting, and experiment management.
Ability to reason from first principles and solve ambiguous technical problems.

Preferred Skills:

CUDA C/C++ or Triton kernel development.
C++ exposure for performance-critical AI systems.
Distributed training using PyTorch DDP, TensorFlow Distributed Strategy, JAX, NCCL, MPI, or similar tools.
Experience with GPU profiling tools such as:

o NVIDIA Nsight Systems

o NVIDIA Nsight Compute

o PyTorch Profiler

o TensorFlow Profiler

Experience with inference optimization, quantization, TensorRT, ONNX, XLA, or model compilation.
Exposure to LLMs, RAG systems, agents, embeddings, multimodal AI, or AI evaluation frameworks.
Understanding of FP32, FP16, BF16, INT8, tensor cores, and numerical stability.

What Excellent Looks Like:

Can implement a model from a research paper without blindly copying code.
Can debug why a model is not learning.
Can explain why GPU utilization is low.
Can identify whether a workload is compute-bound, memory-bound, or communication-bound.
Can write clean, reproducible, and testable Python code.
Can profile before optimizing.
Can work across model logic, GPU execution, data pipelines, and inference systems.
Can communicate technical findings clearly and precisely.

Ideal Candidate Profile:

The ideal candidate is an early-career AI engineer who is not limited to using ML libraries as black boxes.
They understand how models train, how tensors move, how GPUs execute workloads, and how performance, quality, reliability, and safety come together in real AI systems.
The strongest candidates will show hands-on projects in model implementation, GPU acceleration, CUDA/Triton operators, distributed training, LLM evaluation, inference optimization, or research paper reproduction.

Quick Apply

Refine Your Search

ai algorithms transformer architecture jobs in san francisco, ca

AI Engineers

AI Researcher (Computer Vision/Multimodal/Generative AI)

AI Engineer, LLMs

AI/ML Infrastructure Engineer

Robotic AI Engineer/Applied Scientist - Foundation Models

Machine Learning Engineer (Computer Vision/Multimodal/Generative AI)

AI Researcher, Core ML (Turbo)

AI Researcher / ML Engineer (ASR & Speech Specialist)

Fellow Software Engineer-Eng (Agentic AI Architecture & Engineering)

Lead, AI Engineering

Application Engineer – Computer Vision

Machine Learning Engineer

AI Senior Scientist

Research Scientist, Interpretability

Machine Learning Engineer, Integrity

Machine Learning Infrastructure Engineer

SDE- ML Engineer, Frontier AI Robotics

Senior Research Scientist

AI Inference Engineer

Research Engineer, Core ML

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