Staff AI/ML Software Engineer, Model Distillation, Fine-Tuning

Jobtailor

California (MO)

Hybrid

USD 180,000 - 260,000

Full time

4 days ago

Be an early applicant

Application generator

An application made for this job — a tailored resume and cover letter that speak straight to the posting.

Get past ATS filters

Job summary

Jobtailor is seeking a senior ML engineer to design and build knowledge distillation pipelines, transferring reasoning, vision, and language capabilities into compact edge-deployable architectures. You will guide ML architecture decisions and lead edge-optimized model strategies across teams.

You will apply parameter-efficient fine-tuning (LoRA/QLoRA), implement quantization-aware training, and collaborate on data collection, dataset curation, and model evaluation for safe, scalable in-vehicle

Qualifications

Bachelor's degree in Computer Science, Machine Learning, Data Science, Mathematics, or equivalent practical experience
8+ years of software engineering or applied ML research experience
Experience setting technical direction, making architectural decisions on ML systems, and guiding other engineers
Based in or willing to work hybrid out of Mountain View, CA or Seattle, WA, reporting to the office at least three days per week
Master's degree or Ph.D. in Computer Science, Artificial Intelligence, or related field preferred

Responsibilities

Design and build knowledge distillation pipelines transferring reasoning, vision, and language capabilities into compact edge-deployable architectures
Apply and scale parameter-efficient fine-tuning techniques such as LoRA and QLoRA
Build and own the reinforcement learning flywheel using human-in-the-loop alignment with RLHF/DPO
Connect in-cabin data collection with continuous model improvement
Curate, evaluate, and synthetically generate datasets for passenger intent and complex in-vehicle visual cues
Implement Quantization-Aware Training and related techniques to prevent accuracy degradation during hardware compression
Establish evaluation frameworks and benchmarks measuring hallucination rates, domain accuracy, and safety constraints
Own the base model strategy and decide which foundation architectures to use
Set architectural direction for model optimization pipelines as an individual-contributor technical leader
Validate AI capabilities on representative vehicle hardware and chart practical paths to scale

Skills

PyTorch

Knowledge Distillation

Fine-Tuning

Quantization

Reinforcement Learning

Edge Deployment

Model Compression

Open-source

Communication

Education

Bachelor's degree

Master's degree

PhD

Tools

Hugging Face

DeepSpeed

Ray

Megatron

Job description

Design and build knowledge distillation pipelines transferring reasoning, vision, and language capabilities into compact edge-deployable architectures
Apply and scale parameter-efficient fine-tuning techniques such as LoRA and QLoRA
Build and own the reinforcement learning flywheel using human-in-the-loop alignment with RLHF/DPO
Connect in-cabin data collection with continuous model improvement
Curate, evaluate, and synthetically generate datasets for passenger intent and complex in-vehicle visual cues
Implement Quantization-Aware Training and related techniques to prevent accuracy degradation during hardware compression
Establish evaluation frameworks and benchmarks measuring hallucination rates, domain accuracy, and safety constraints
Own the base model strategy and decide which foundation architectures to use
Set architectural direction for model optimization pipelines as an individual-contributor technical leader
Validate AI capabilities on representative vehicle hardware and chart practical paths to scale

Requirements

Bachelor's degree in Computer Science, Machine Learning, Data Science, Mathematics, or equivalent practical experience
8+ years of software engineering or applied ML research experience
Experience setting technical direction, making architectural decisions on ML systems, and guiding other engineers
Deep proficiency in PyTorch
Hands-on experience fine-tuning large language models or vision-language models
Practical experience with at least two of knowledge distillation, parameter-efficient fine-tuning, pruning, or quantization
Based in or willing to work hybrid out of Mountain View, CA or Seattle, WA, reporting to the office at least three days per week
Master's degree or Ph.D. in Computer Science, Artificial Intelligence, or a related field (preferred)
Experience shipping a quantized model to a specific hardware target
Familiarity with Hugging Face, DeepSpeed, Ray, or Megatron
Experience managing dataset pipelines at scale
Domain experience in conversational AI, human-computer interaction, smart spaces, or deploying multimodal models in consumer-facing products
Open-source contributions or published research in model compression, distillation, or efficient AI
Ability to communicate complex AI training concepts and architectural trade-offs to cross-functional teams

Core Competencies

Demonstrates expertise in designing and optimizing machine learning architectures, particularly in knowledge distillation and reinforcement learning. Proficient in fine-tuning large language models and implementing quantization techniques for edge deployment.

Highest-signal resume keywords