Lead AIML Engineer
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Job Location:
Mountain View, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
2 days ago
Vacancies:
1 Vacancy
Job Summary
At ASAPP our mission is simple: deliver the best AI-powered customer experiencefaster than anyone else. To achieve that were guided by principles that shape how we think build and execute. We value customer obsession purposeful speed ownership and a relentless focus on outcomes. ASAPPs AI Engineering team is seeking an enterprising talented and curious machine learning engineer.
We are seeking a highly experienced Lead AI/ML Engineer to join our Core GenerativeAgent team. You will play a pivotal role in designing building and deploying cutting-edge AI systems that power mission-critical enterprise applications. This role is ideal for an individual who thrives in ambiguity is deeply technical and has a strong product sense paired with deep expertise in foundational models and enterprise AI systems.
You will lead the design and delivery of end-to-end voice AI solutions combining large language models with speech technologies such as speech-to-text text-to-speech and real-time streaming audio pipelines. This role requires a hands-on technical leader who can architect low-latency highly reliable conversational voice systems and guide a team through ambiguity toward production excellence.
We are looking for someone who understands the unique constraints of voice experiences latency turn-taking interruption handling streaming inference and audio quality and can translate these into scalable enterprise-grade systems.
This is a hybrid role with weekly in-person responsibilities. We have offices in New York City and Mountain View CA
What youll do
- Build real-time conversational AI systems including voice interfaces powered by speech-to-text text-to-speech and streaming inference pipelines
- Design and optimize low-latency inference workflows for multimodal applications involving text speech and real-time interactions
- Integrate and apply foundation models from major providers (OpenAI AWS Bedrock Anthropic etc.) for prototyping and production use cases
- Adapt evaluate and optimize LLMs for domain-specific enterprise applications
- Build and maintain infrastructure for experimentation deployment and monitoring of AI models in production
- Improve model performance and inference workflows with attention to latency cost and reliability
- Provide technical leadership within the team mentoring engineers and promoting best practices in ML engineering
- Partner with product and cross-functional stakeholders to translate requirements into scalable ML solutions
- Contribute to the evolution of internal standards for experimentation evaluation and deployment
What youll need
- 6 years of experience in Machine Learning or AI systems with hands-on experience in LLMs speech or conversational AI systems
- Experience building on integrating speech-to-text and text-to-speech systems
- Strong experience integrating voice models into production applications
- Proficiency on Python and ML frameworks like PyTorch or TensorFlow
- Proven experience leading complex cross-functional AI initiatives
- Deep understanding of latency-sensitive system design and distributed architectures
- Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Understanding of RAG pipelines prompt engineering and vector search
- Experience deploying and scaling AI systems using AWS (required) Docker Kubernetes and CI/CD practices
- Strong communication skills with the ability to align engineering product and executive stakeholders
- Comfortable operating in fast-paced environments and driving clarity in ambiguous problem spaces
What wed like to see
- Experience with speech model fine-tuning and acoustic/language model optimization
- Experience with production applications of S2S models
- Hands-on experience with real-time or streaming audio systems (WebRTC gRPC streaming or similar architectures)
- Experience optimizing TTS prosody pronunciation control and voice customization
- Background in MLOps experimentation platforms or evaluation frameworks for speech and conversational systems
- Contributions to open-source AI or speech tooling
- Graduate degree (MS or PhD) in Computer Science Machine Learning Speech Processing or related field
$170000 - $190000 a year
Compensation package also includes a performance bonus on top of the listed salary range
Separately we also offer a compelling equity grant comprised of stock options
Benefits include:
Competitive compensation with stock options
Comprehensive medical vision and dental insurance
401k matching
Fitness and wellness stipend
Mental well-being benefits
Professional learning and development stipend
Parental leave including adoptive and foster parents
3 weeks paid time off (increases with tenure) along with sick leave bereavement and jury duty
ASAPP is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race color religion gender gender identity or expression sexual orientation national origin disability age or veteran status. If you have a disability and need assistance with our employment application process please email us at emailprotected to obtain assistance. #LI-AG1 #LI-Hybrid
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
IC
Key Skills
- Administrative Skills
- Facilities Management
- Biotechnology
- Creative Production
- Design And Estimation
- Architecture
About Company
Improve customer experience and radically increase CX performance at the same time. This AI-Native® software platform provides AI-driven predictions on what agents should and do throughout each interaction and increasingly automates routine tasks before, during, and after the conversa ... View more
