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Founding ML Engineer
Founding ML Engineer

Founding ML Engineer at The Persona Labs · Bhilai, Raipur · 0 - 4 years · ₹7L - ₹13L / yr (ESOP available) · Posted 28 Sep 2026

The Persona Labs's logo

Founding ML Engineer

Saif Khan's profile picture
Posted by Saif Khan
0 - 4 yrs
₹7L - ₹13L / yr (ESOP available)
Bhilai, Raipur
Skills
skill iconPython
PyTorch
TensorFlow
skill iconPostgreSQL
Retrieval Augmented Generation (RAG)
SQL
Data modeling
Behavioral modeling
skill iconData Analytics
RESTful APIs
skill iconDocker
skill iconGitHub
Software deployment
System deployment
Graph Databases
skill iconRedis

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.


We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.


DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP


THE AI LAYER - COMPANION INTELLIGENCE

Alongside the platform, we are building a proactive personal AI companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders, conversations, and experiences. 


THE OPPORTUNITY

We are looking for a Founding ML Engineer to build the intelligence layer of the platform from the ground up. This is a 0→1 Applied AI / ML role where you will work directly with the founder and Product Engineer to turn ambiguous problems around users, relationships, recommendations and personal intelligence into working systems.


You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.


WHAT YOU WILL BUILD & OWN


USER INTELLIGENCE

User representations, behavioural models, interests, preferences, contextual signals, and evolving understanding of the user. MEMORY Short- and long-term memory, episodic/preference/relationship memory, retrieval, relevance and updating.


RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.


INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.


RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.


CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.


RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.


NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.


TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do. 


WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.


WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work. 


WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?


RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?


AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?


USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?


SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?


EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?


PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?


SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?


BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.


CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

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About The Persona Labs

Founded
Type
Size
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About

Personalized. Positive. Meaningful. A studio building human-first digital products designed around people, not patterns.
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·       Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

·       GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

·       Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

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·       Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).


Preferred Qualifications

·       Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

·       Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

·       Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

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Arpita Pathak
Posted by Arpita Pathak
Indore, Pune, Ahmedabad
4 - 6 yrs
₹7L - ₹10L / yr
skill iconPython
skill iconMachine Learning (ML)
Artificial Intelligence (AI)
Generative AI
Large Language Models (LLM) tuning
+5 more

Experience - 4 to 6 year

Location – Ahmedabad/Pune/Indore

  • Additional Job Description

Additional Job Description

Required Skills and Experience: 

  • Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
  • Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.
  • Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
  • Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
  • Demonstrated experience implementing content filtering / moderation systems.
  • Solid skills working with structured and unstructured data and advanced feature engineering.
  • Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
  • Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
  • Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
  • Good knowledge of security, data governance, and privacy best practices for AI systems.


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