{"id":4219509,"url":"https://alion.io/job/salesforce-lmts-lead-aiml-engineer-llm-post-training","title":"LMTS - Lead AI/ML Engineer (LLM Post-Training)","company":{"id":70,"name":"Salesforce","domain":"salesforce.com","url":"https://alion.io/company/salesforce","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":81,"open_postings":401,"ghost_share":0.022,"stale_share":0.658,"repost_share":0.047,"time_to_fill_p50_days":31,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentforce","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GRPO","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Model Distillation","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Post-training","optional":false},{"name":"Pre-training","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SFT","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tokenization","optional":false},{"name":"Accelerate","optional":true},{"name":"AI Agents","optional":true},{"name":"DeepSpeed","optional":true},{"name":"FSDP","optional":true},{"name":"PEFT","optional":true},{"name":"PPO","optional":true},{"name":"PyTorch","optional":true},{"name":"Transformers","optional":true},{"name":"TRL","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-13T04:06:03Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T04:06:03Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"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.\nJob Category\nSoftware EngineeringJob Details\nAbout Salesforce\nSalesforce 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.\nReady 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.\nWhat You'll Be Doing & Why It Matters\n\nSalesforce 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.\nThis 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.\nThe 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.\nWe 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.\nCore Responsibilities & Scope\nTrain, adapt, and serve capable, reliable open-weight language models for enterprise workflows.\nOwn 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.\nGenerate 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.\nBuild 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.\nDesign 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.\nDesign 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.\nInvestigate 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.\nBuild and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow.\nBuild reliable model-serving and inference capabilities, balancing quality, latency, throughput, memory use, and operational reliability.\nBuild robust integrations between models, tools, and enterprise systems, including structured outputs, function calling, and multi-step agent behavior.\nMake architecture and design decisions with strong attention to model quality, reproducibility, performance, security, scalability, and future extensibility.\nImprove observability, traceability, failure analysis, test coverage, and developer workflows across the training and serving lifecycle.\nWork with domain experts to turn real workflows, product behavior, APIs, and failure cases into independently verifiable training and evaluation material.\nProtect customer and company data: use approved sources only, preserve provenance, and keep credentials and sensitive data out of training and model prompts unless specifically authorized.\nReview model, data, and application changes for factual correctness, quality, security, performance, and operational risk.\nMentor engineers and provide technical guidance that raises the quality bar across the team.\nMinimum Requirements\n10+ years of software engineering experience, including technical leadership on production systems.\nStrong Python engineering experience building and maintaining reliable production systems.\nHands-on ownership of LLM post-training. You have trained or materially improved open-weight language models through SFT and LoRA/QLoRA. Experience with preference optimization, RL, distillation, or continued pre-training is strongly valued. API integration alone does not qualify.\nHands-on ownership of training-data generation and curation. You have produced, filtered, reviewed, and versioned task-specific datasets, including synthetic instruction data, conversations, reasoning traces, tool calls, preference data, or structured outputs.\nStrong practical understanding of the post-training stack: chat templates, tokenizer behavior, sequence packing, loss masking, optimizer and learning-rate schedules, checkpointing, distributed or memory-efficient training, and inference tradeoffs.\nExperience creating and evaluating reasoning, tool-use, or structured-output training data. You understand the difference between a plausible trace and a correct, useful outcome, and know how low-quality traces degrade a model.\nExperience with model evaluation: held-out datasets, error taxonomy, task and quality metrics, experiment design, ablation studies, regression analysis, and human-review workflows.\nStrong software-engineering fundamentals: API design, testing, debugging, observability, version control, and clear technical writing.\nAbility to investigate ambiguous failures across model behavior, data, prompts, retrieval, tools, and backend services.\nDemonstrated leadership in architecture, design reviews, code reviews, and mentoring engineers.\nComfortable working across application and ML layers instead of staying within a narrow specialty.\nA demonstrated, genuine AI-first approach to engineering - using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.\nExperience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows.\nAdvanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.\nPreferred Qualifications\nExperience building integrations with enterprise platforms, APIs, data systems, identity controls, or tool-using AI applications.\nExperience with complex enterprise workflows, such as CRM, commerce, finance, service, supply chain, or regulated-industry systems.\nDeep experience with preference optimization or reinforcement-learning methods for LLMs, such as DPO, ORPO, GRPO, PPO, or verifiable-reward training. Candidates must be able to explain the reward/preference data, training stability, and failure modes, not just name a framework.\nResource-constrained or distributed model training and inference, including PyTorch, Transformers, TRL, Accelerate, DeepSpeed, FSDP, vLLM, GPU optimization, or quantized model serving.\nRetrieval systems, source provenance, and retrieval evaluation. Retrieval experience is useful, but candidates must understand that retrieval is context, not a substitute for model training or model evaluation.\nSecurity and data-governance work involving sensitive enterprise data, access scopes, auditability, and human approval boundaries.\nExperience taking an AI feature from experiment through a measured, monitored production release.\nWhat We Will Ask You To Show\nPlease be ready to discuss work you personally did. Strong candidates can explain:\nOne model-training project they owned end to end: base model, training objective, dataset scale and provenance, compute setup, training recipe, evaluation, production outcome, and what failed.\nOne dataset they generated for fine-tuning: source material, generation method, filtering and review, split strategy, known failure modes, and the evidence that it improved the model.\nThe actual training method they used, including why it was chosen over alternatives, how they tuned it, and how they knew it improved behavior rather than merely format-following.\nOne reasoning, tool-use, preference, or reinforcement-learning dataset they created or used: how correctness was established, how contamination was prevented, and how reward hacking or low-quality traces were detected.\nThe exact denominator, dataset split, and review method behind any quality or business-impact metric on their resume.\nA difficult production incident or model-quality regression, how they isolated the cause, and the evidence that confirmed the fix.\nA code sample, design document, experiment report, publication, or other artifact that makes their contribution verifiable. Confidential details may be redacted.\nWe do not require a PhD, publications, or prior domain experience. We do require depth, honesty about individual contribution, and the ability to turn claims into technical evidence.\nUnleash Your Potential\nWhen you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance andbe your best, and our AI agents accelerate your impact so you cando your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future - but to redefine what’s possible - for yourself, for AI, and the world.\nAccommodations\nIf you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.\nPlease note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.\nPosting Statement\nSalesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications - without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. 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