AI/ML Engineer

AI/ML Engineer — LockedIn AI

Job Type: Full-Time
Work Model: Remote (US-Based) / Optional Hybrid (New York, NY)
Compensation: $150,000 – $220,000 USD per year

About the Company
LockedIn AI is a fast-growing AI career technology company building real-time interview and meeting copilot systems used by over 1M users worldwide. The platform helps candidates perform better in job interviews, coding assessments, and professional meetings through advanced AI assistance.

Role Overview
We are hiring a production-focused AI/ML Engineer to design, build, train, and deploy machine learning systems powering real-time AI copilots. This is a full-stack ML role covering model development, training pipelines, deployment, inference optimization, and production monitoring.

You will work across LLMs, speech models, retrieval systems, and scalable AI infrastructure to deliver low-latency, high-quality AI experiences.

Key Responsibilities
Model Development & Training
Design, train, and fine-tune ML models including LLMs, NLP systems, and speech-to-text models
Apply techniques such as LoRA, QLoRA, RLHF, and DPO
Build and optimize training datasets and evaluation pipelines
Run experiments, ablation studies, and performance benchmarking
Production Deployment & Inference
Build real-time inference pipelines optimized for low latency
Deploy models using Docker, Kubernetes, and CI/CD workflows
Integrate multiple LLM providers with routing and fallback systems
Implement caching, quantization, and streaming optimization
NLP, RAG & AI Systems
Build retrieval-augmented generation (RAG) pipelines using vector databases
Develop NLP features like classification, summarization, and semantic search
Design prompt engineering systems and multi-turn AI workflows
Create agent-based systems with tool use and function calling
Monitoring & Evaluation
Build automated evaluation frameworks and LLM benchmarking systems
Track latency, hallucination rate, cost, and performance metrics
Implement drift detection and production monitoring tools
Optimize inference cost and model efficiency
Data & Infrastructure
Build scalable data pipelines for training and inference
Maintain data versioning, logging, and experiment tracking systems
Collaborate with data engineering teams on infrastructure scaling
Safety & Responsible AI
Implement safety filters and guardrails for AI outputs
Monitor bias, fairness, and robustness of models
Ensure privacy-first and secure AI system design

Required Qualifications
3+ years in ML engineering or production AI systems
Strong Python and ML framework experience (PyTorch, TensorFlow, JAX)
Deep understanding of transformers, deep learning, and optimization
Experience with LLM APIs (OpenAI, Anthropic, or open-source models)
Hands-on experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure)
Experience building production ML systems end-to-end

Preferred Qualifications
Experience with real-time AI or low-latency systems
Knowledge of model compression, quantization, or distillation
Experience with RAG systems and vector databases
Familiarity with agentic AI workflows and tool-use systems
Contributions to open-source ML projects or research

What We Offer
Meaningful early-stage equity ownership
High-impact role serving 1M+ users
Remote-first flexibility
Fast-paced, AI-native engineering culture
Opportunity to own end-to-end ML systems

How to Apply
Submit your resume along with a short note explaining your interest in the role, relevant experience, and any ML or AI projects or portfolio work.