# LMTS - Lead AI/ML Engineer (LLM Post-Training) at Salesforce

Salesforce is hiring an LMTS - Lead AI/ML Engineer (LLM Post-Training) in Bengaluru, India. Level rates it Builds AI ●●●●; you can [apply on Salesforce](https://jobsbylevel.com/go/dd4c1caf-d0a3-4a03-aef2-2f55de6f5349?utm_medium=ai-agent).

AI Level 4, AI centrality 99 out of 100. India - Bangalore.

## Details

- Company: [Salesforce](https://jobsbylevel.com/companies/salesforce)
- AI level: AI Level 4 (score 99 out of 100)
- Location: India - Bangalore
- Posted: November 13, 2026
- Apply: https://jobsbylevel.com/go/dd4c1caf-d0a3-4a03-aef2-2f55de6f5349?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=lmts-lead-ai-ml-engineer-llm-post-training-at-salesforce-2d0977

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/lmts-lead-ai-ml-engineer-llm-post-training-at-salesforce-2d0977

## Description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. What You'll Be Doing & Why It Matters Salesforce has immediate opportunities for engineers who want their work to shape how trusted enterprise AI is trained, evaluated, and delivered. You will work with a group of world-class engineers to train and post-train open-weight language models for demanding enterprise workflows. This is a model-training role, not a wrapper around a hosted LLM and not a prompt-engineering-only role. You will own the complete post-training loop: data generation, supervised fine-tuning, preference or reinforcement learning when appropriate, rigorous evaluation, and production serving. You will materially improve the behavior of models, not merely integrate model APIs. The role encompasses ML research engineering, data generation, fine-tuning, evaluation, inference systems, and technical leadership. You will own code, experiments, evaluation evidence, and production behavior. We value demonstrated engineering judgment and reproducible results over credentials, paper claims, or an extensive keyword list. We pride ourselves on building high-quality, maintainable systems that make the model more capable without compromising trust, safety, or operability. We embrace the hybrid model and celebrate the individual strengths of each team member while encouraging everyone to grow into the best version of themselves. We believe autonomous teams with the freedom to make decisions empower our people, products, company, and customers to thrive. Core Responsibilities & Scope Train, adapt, and serve capable, reliable open-weight language models for enterprise workflows. Own fine-tuning experiments from baseline through release: choose training objectives, run SFT and LoRA/QLoRA recipes, compare checkpoints against rigorous held-out evaluation, and diagnose failures before the next run. Generate high-quality domain training data at scale, including synthetic instruction data, multi-turn conversations, reasoning and tool-use trajectories, structured outputs, counterexamples, preference pairs, and difficult evaluation cases. Build the data engine behind post-training: source-grounded authoring, quality filtering, deduplication, contamination controls, dataset versioning, train/validation/test splits, packing, tokenization, and loss masking. Design and run post-training experiments beyond SFT when justified, including DPO, ORPO, GRPO, RLVR, distillation, and other preference or reinforcement-learning methods. Match the method to a measured capability problem and a defensible reward signal. Design and operate model-quality evaluation: held-out task suites, model-based and human review, tool-use and structured-output accuracy, multi-turn reliability, safety regressions, ablations, and error analysis. Investigate model behavior deeply: identify whether a failure comes from data quality, training objective, model capacity, decoding, context handling, tool interface, or serving behavior, then produce evidence for the corrective action. Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow. Build reliable model-serving and inference

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Source: https://jobsbylevel.com/jobs/lmts-lead-ai-ml-engineer-llm-post-training-at-salesforce-2d0977

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