Recruiting from Scratch
Applied Machine Learning Engineer
Recruiting from Scratch, San Francisco, California, United States, 94199
Who is
Recruiting from Scratch :Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.
Applied Machine Learning EngineerAbout UsWe're a cutting-edge technology company revolutionizing the sales industry by transforming sales representatives from manual laborers into scientists. Our AI-powered platform combines automation and real-time collaboration tools to dramatically increase sales productivity, often resulting in a 2-3x boost within weeks of implementation.
Founded in 2020 by AI experts from Stanford, our team of ~50 includes engineering talent from top tech companies and sales professionals from industry leaders. We've secured $27M in funding and are on a rapid growth trajectory, having scaled from $0 to ~$5M ARR in just two years.
The RoleWe're seeking an Applied Machine Learning Engineer to join our innovative team. This key role will focus on implementing ML features into our platform, contributing to our ambitious vision of AI-powered real-time collaboration in sales.
Location & Work Arrangement
San Francisco (Financial District)
Hybrid work model (2-3 days/week in office)
Compensation
Salary: $170k - $250k
Equity: 0.04-0.1%
Full-time, W-2 position
Visa sponsorship available
Responsibilities
Implement and deploy ML models in production environments
Train production models to improve accuracy for specific sales use cases
Align technical strategy with performance, cost, and feasibility considerations
Collaborate on solving complex challenges in AI-powered real-time collaboration
Contribute to the development of smart call funnels, playbooks, and conversation analysis tools
Required Qualifications
3+ years of experience, including 2+ years training and deploying ML models in production
Strong background in Computer Science, Machine Learning, or related field from a top-tier university
Expertise in Python, PyTorch, and Kubernetes AI inference stack
Proficiency with Transformers, LLMs (open-source and public frameworks), and deep audio foundation models
Experience with causal inference and few-shot learning techniques
Preferred Qualifications
Background in sales technology or conversational AI
Experience with real-time audio AI and precision/recall/latency tradeoffs
Familiarity with GPT-3 and other advanced language models
Knowledge of conversation embeddings and Markov models
Technical Challenges You'll Tackle
Real-time audio AI for call classification (human, voicemail, dial tree) with strict latency requirements
Smart call funnels and playbooks using GPT-3 and other LLMs to derive actionable strategies from unstructured call data
Conversation embeddings and Markov models to predict and optimize call outcomes
LLM-based systems for sales process automation and optimization
Our Tech Stack
Python, PyTorch, Kubernetes
Transformers and Large Language Models
Deep audio foundation models
Causal inference frameworks
Few-shot learning techniques
#J-18808-Ljbffr
Recruiting from Scratch :Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.
Applied Machine Learning EngineerAbout UsWe're a cutting-edge technology company revolutionizing the sales industry by transforming sales representatives from manual laborers into scientists. Our AI-powered platform combines automation and real-time collaboration tools to dramatically increase sales productivity, often resulting in a 2-3x boost within weeks of implementation.
Founded in 2020 by AI experts from Stanford, our team of ~50 includes engineering talent from top tech companies and sales professionals from industry leaders. We've secured $27M in funding and are on a rapid growth trajectory, having scaled from $0 to ~$5M ARR in just two years.
The RoleWe're seeking an Applied Machine Learning Engineer to join our innovative team. This key role will focus on implementing ML features into our platform, contributing to our ambitious vision of AI-powered real-time collaboration in sales.
Location & Work Arrangement
San Francisco (Financial District)
Hybrid work model (2-3 days/week in office)
Compensation
Salary: $170k - $250k
Equity: 0.04-0.1%
Full-time, W-2 position
Visa sponsorship available
Responsibilities
Implement and deploy ML models in production environments
Train production models to improve accuracy for specific sales use cases
Align technical strategy with performance, cost, and feasibility considerations
Collaborate on solving complex challenges in AI-powered real-time collaboration
Contribute to the development of smart call funnels, playbooks, and conversation analysis tools
Required Qualifications
3+ years of experience, including 2+ years training and deploying ML models in production
Strong background in Computer Science, Machine Learning, or related field from a top-tier university
Expertise in Python, PyTorch, and Kubernetes AI inference stack
Proficiency with Transformers, LLMs (open-source and public frameworks), and deep audio foundation models
Experience with causal inference and few-shot learning techniques
Preferred Qualifications
Background in sales technology or conversational AI
Experience with real-time audio AI and precision/recall/latency tradeoffs
Familiarity with GPT-3 and other advanced language models
Knowledge of conversation embeddings and Markov models
Technical Challenges You'll Tackle
Real-time audio AI for call classification (human, voicemail, dial tree) with strict latency requirements
Smart call funnels and playbooks using GPT-3 and other LLMs to derive actionable strategies from unstructured call data
Conversation embeddings and Markov models to predict and optimize call outcomes
LLM-based systems for sales process automation and optimization
Our Tech Stack
Python, PyTorch, Kubernetes
Transformers and Large Language Models
Deep audio foundation models
Causal inference frameworks
Few-shot learning techniques
#J-18808-Ljbffr