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About This Role
Role: Software Engineer \- Machine Learning III
Location: Mountain View, CA (Onsite: M\-F)
Duration: 12 Months Contract
Pay Rate: $110/hr W2
Job ID: 37409930
Client: Samsung Research America
Position Summary:
- We are looking for an experienced Machine Learning Engineer to lead the development of prompt injection and prompt safety models that protect Samsung's downstream agentic AI systems across phone, cloud, and XR/AR.
- You will design, train, and deploy classifier and guardrail models (both cloud\-based and hybrid on\-device) that screen agent inputs and outputs for injection attacks, unsafe content, and policy violations. A core part of the role is post\-training these models with RLHF, DPO, and related optimization techniques to push detection accuracy and false\-positive rates beyond what off\-the\-shelf solutions provide.
Role and Responsibilities:
- Design and train prompt injection detection models and prompt safety classifiers that operate on both inputs to and outputs from Samsung's agentic AI systems.
- Build hybrid deployment pipelines that split safety inference between on\-device (phone, XR/AR) and cloud, optimizing for latency, privacy, and detection coverage.
- Apply post\-training techniques (e.g. RLHF, reward modeling, policy optimization) to optimize guardrail model performance, calibration, and robustness against adaptive adversaries.
- Curate and generate adversarial training data: direct and indirect prompt injections, jailbreaks, tool\-use exploits, and unsafe\-output cases drawn from red\-teaming and production signals.
- Build evaluation harnesses that measure attack success rate, false\-positive rate, latency, and on\-device footprint across model iterations and threat categories.
- Partner with agent, device, and platform teams to integrate safety models into mobile\-use agents, XR/AR assistants, and cloud agentic workflows, and to close the loop from production incidents back into training data.
- Work cross\-functionally with security researchers, modeling teams, and product engineers; document methods and, where appropriate, contribute to patents and publications.
Required Qualifications:
- M.S. or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field; or B.S. with equivalent industry experience.
- 3\+ years of industry experience in ML engineering or applied AI research, with demonstrated ownership of production ML systems post masters degree graduation
- 2\+ years of industry experience in software engineering post masters degree graduation
- Strong proficiency in Python and PyTorch (or JAX/TensorFlow), with solid software engineering fundamentals (version control, testing, and reproducible experimentation).
- Hands\-on experience post\-training LLMs with RLHF, DPO, RLAIF, or reward modeling including reward design, preference data curation, and training stability.
- Hands\-on experience training and deploying classifier or guardrail models for safety, content moderation, abuse detection, or adversarial robustness.
- Familiarity with prompt injection, jailbreak, and agentic AI threat models, and with distributed training frameworks (DeepSpeed, FSDP, Accelerate).
Preferred Qualifications:
- Experience building safety or moderation systems for agentic AI: tool\-use guardrails, indirect prompt injection defenses, or output filtering for autonomous agents.
- Experience with red\-teaming, adversarial data generation, or automated attack pipelines (e.g., GCG, PAIR, generator–critic frameworks)
- Experience with on\-device or edge ML deployment (ExecuTorch, Core ML, TFLite, MLC\-LLM, vendor NPU toolchains) and model compression (quantization, distillation, pruning) for safety models.
- Experience with telemetry, logging, or user\-facing data systems on mobile, XR/AR, or consumer platforms, including privacy\-preserving handling of user data (e.g., anonymization, on\-device processing, federated approaches).
- Publications at top\-tier ML/NLP/security venues (NeurIPS, ICML, ICLR, ACL, EMNLP, USENIX Security, IEEE S\&P), patents, or open\-source contributions in the safety, alignment, or AI security space.
Education:
- M.S. in Computer Science, Machine Learning, Electrical Engineering, or a related field with 3 years of experience post\-graduation
- Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field with publications in the AI/ML domain and 1 year of experience post\-graduation
Pay: $105\.00 \- $110\.00 per hour
Work Location: In person
Salary Context
This $218K-$228K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NuByt, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $218K to $228K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NuByt AI Hiring
NuByt has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $228K - $228K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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