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About This Role
We're Hiring: Senior Machine Learning Research Engineer
San Francisco, CA \| Full\-Time \| On\-site
- Base Salary: $250K\+ Base with Competitive Equity
- Visa Sponsorship Available
About the Company
Join one of the fastest\-growing AI startups revolutionizing the future of Audio AI. Founded by former Scale AI engineers and backed by leading investors including NVIDIA, the company is building high\-quality audio datasets that power the world's leading AI labs and frontier models. Working at the intersection of cutting\-edge research and real\-world AI applications, you'll have the opportunity to shape the next generation of Speech and Multimodal AI technologies.
We're looking for exceptional Machine Learning Research Engineers who are passionate about advancing the frontier of Speech, Audio, and Multimodal AI while taking complete ownership of research from concept to production.
What We're Looking For
Required Experience
- PhD from a Top 25 Computer Science program with at least 1 year of Startup industry experience,
OR
- 5\+ years of startup industry experience in Machine Learning Research with a strong publication record (including internal research publications).
- Proven experience across the entire Machine Learning lifecycle, including:
- Research \& Design
- Model Training
- Fine\-tuning
- Deployment
- Strong ownership mindset with the ability to lead research initiatives end\-to\-end rather than contributing to only a single stage of the ML pipeline.
Preferred Qualifications
Publications at premier AI conferences such as:
- NeurIPS
- ICML
- ICLR
Research experience in one or more of the following:
- Speech AI
- Audio AI
- Speech\-to\-Text
- Text\-to\-Speech
- Multimodal LLMs
- Image, Video \& Text Models
- Model Evaluation (Evals)
Background combining:
- High\-growth startup experience
- Large technology companies
- Top AI labs or research organizations
Ideal candidates have experience across both high\-growth startups and large technology companies, bringing the agility of startups together with the scale and engineering excellence of enterprise environments.
Preferred experience includes organizations such as OpenAI, Anthropic, DeepMind, Meta FAIR, xAI, Apple, Gemini, Scale AI, Inworld, or similar research\-focused environments.
Tech Stack
- Python
- PyTorch
- Deep Learning
- Audio \& Speech ML
- Digital Signal Processing (DSP)
- ML Pipelines
- Cloud Infrastructure
- Large Neural Network Training
- Production ML Deployment
Not the Right Fit If You...
- Primarily work in ML Infrastructure or ML Platform Engineering.
- Have experience limited to inference, recommendation systems, or only one phase of the ML lifecycle.
- Prefer contributing to isolated components instead of leading research end\-to\-end.
\#Hiring \#MachineLearning \#AIResearch \#ResearchEngineer \#DeepLearning \#Python \#PyTorch \#SpeechAI \#AudioAI \#MultimodalAI \#LLM \#NeurIPS \#ICML \#ICLR \#SanFrancisco \#TechJobs \#HiringNow
Pay: From $250,000\.00 per year
Application Question(s):
- Do you have 5\+ years of ML Research startup experience (or a PhD \+ 1 year industry experience), including end\-to\-end model development in a high\-growth startup or AI lab? (Mandatory)\*
- Do you have hands\-on experience with Python, PyTorch, and end\-to\-end ML model development for Speech, Audio, or Multimodal AI? (Mandatory)
- Please confirm your potential availability in the given format to conduct initial screening, (DDMM 24 Hour Clock Format Time (e.g 14;00\) \*\*Example (04081700\)\*\* (Mandatory for initial screening)
- What is your current compensation (Per Anum) ?
- What is your expected compensation (Per Anum) ?
- If you did not provide your LinkedIn in Resume, Please paste the link below as it is mandatory.
Education:
- Doctorate (Required)
Work Location: In person
Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 4,317 AI roles we're tracking, Research Engineer positions make up 2% of the market. At carnaby fox, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $272,100 based on 227 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and AI Engineering Manager ($244,000). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
carnaby fox AI Hiring
carnaby fox has 1 open AI role right now. They're hiring across Research Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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