{"id":1963380,"url":"https://alion.io/job/whitetable-machine-learning-engineer-ai-research-2","title":"Machine Learning Engineer - AI Research","company":{"id":3800729,"name":"Whitetable","domain":"whitetable.ai","url":"https://alion.io/company/whitetable-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":54000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"PPO","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"Weights & Biases","optional":false}],"status":"live","first_seen_at":"2026-10-06T16:18:41Z","employer_posted_date":null,"last_verified_at":"2026-10-06T16:18:41Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Role : Machine Learning Engineer - AI Research\n\nLocation : Bangalore\n\nEmployment Type : Full-time\n\nExperience : 3+ Years\n\nAbout the Role : \n\nWe are looking for a Machine Learning Engineer - AI Research with strong hands-on experience in deep learning, large language models, and model development. The role involves building, fine-tuning, evaluating, and improving AI models for complex, high-accuracy applications.\n\nYou will work closely with product, engineering, and domain experts to solve ambiguous AI problems and take solutions from research and experimentation to production.\n\nKey Responsibilities : \n\n- Model Training & Fine-Tuning : Train and fine-tune deep learning and LLM models using domain-specific datasets.\n\n- Model Evaluation : Design benchmarks, evaluation frameworks, metrics, and error-analysis processes to measure model quality and reliability.\n\n- Reinforcement Learning : Develop and implement RL/RLHF and preference-optimization approaches such as PPO, DPO, or similar techniques.\n\n- Research & Experimentation : Conduct structured experiments across models, datasets, architectures, and training approaches while maintaining reproducible experiment records.\n\n- Problem Solving : Independently identify, scope, and solve open-ended ML problems while balancing accuracy, latency, and infrastructure costs.\n\n- Productionization : Translate successful research experiments into reliable, production-ready ML systems.\n\n- Cross-Functional Collaboration : Work with product, engineering, and domain experts to convert real-world requirements into effective AI solutions.\n\nWhat We're Looking For : \n\n- 3+ years of hands-on experience building, training, and deploying deep learning models in research or production environments.\n\n- Strong experience with deep learning and LLMs, including model training and fine-tuning.\n\n- Practical experience with data curation, model training infrastructure, and large-scale experimentation.\n\n- Experience designing and implementing ML evaluation and benchmarking frameworks.\n\n- Working knowledge of RL, RLHF, PPO, DPO, or preference optimization techniques.\n\n- Strong software engineering and problem-solving fundamentals.\n\n- Experience with experiment tracking tools such as Weights & Biases (W&B), MLflow, or similar platforms.\n\n- Ability to independently work on technically ambiguous problems and take ownership from idea to implementation.\n\n- Strong analytical and communication skills.\n\nPreferred : \n\n- Experience with NLP, Legal AI, document intelligence, or LLM applications.\n\n- Exposure to AI applications in regulated or high-stakes domains.\n\n- Experience taking ML research prototypes into production environments.\n\nWhat You'll Get : \n\n- Opportunity to work on AI-first products involving LLMs and advanced machine learning.\n\n- High ownership and the opportunity to work on challenging, open-ended ML problems.\n\n- Direct collaboration with experienced product, engineering, and domain teams.\n\n- Opportunity to contribute across the full lifecycle - 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