Applied Machine Learning Engineer - Recruiting from Scratch

Applied Machine Learning Engineer

About Us

We'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 Role

We'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

1. Real-time audio AI for call classification (human, voicemail, dial tree) with strict latency requirements
2. Smart call funnels and playbooks using GPT-3 and other LLMs to derive actionable strategies from unstructured call data
3. Conversation embeddings and Markov models to predict and optimize call outcomes
4. 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