{"id":1149421,"url":"https://alion.io/job/sentient-applied-ml-engineer","title":"Applied ML Engineer","company":{"id":21243,"name":"Sentient Technologies","domain":"sentient.ai","url":"https://alion.io/company/sentient","size_band":null,"is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","hiring_geo_confidence":"inferred","locations":["Singapore","Hong Kong","Austin, United States"],"countries":["SG","HK","US"],"hiring_countries":["US","CN","IN","JP","GB","AU","ID","MY","NZ","PK","PH","KR","TW","TH","VN","AS","BD","BT","BN","KH","CK","TL","FJ","PF","GU","KI","LA","MV","MH","MN","NR","NP","NC","NU","NF","MP","PW","PG","PN","WS"],"hiring_countries_total":49,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"React.js","optional":false},{"name":"Transformers","optional":false},{"name":"TypeScript","optional":false},{"name":"DSPy","optional":true},{"name":"Interpretability","optional":true},{"name":"JavaScript","optional":true},{"name":"LiteLLM","optional":true},{"name":"Next.js","optional":true},{"name":"pgvector","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Ray","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-23T13:56:14Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-09-24T06:57:27Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Applied ML Engineer\nThe Role\nWe’re looking for an Applied ML Engineer to build systems at the intersection of machine learning research and production software.\nThis is an end-to-end engineering role. You should be comfortable reading a research paper, identifying what is actually testable, building the smallest useful experiment, evaluating it rigorously, and turning the result into a production system that users can interact with.\nYou’ll work across model evaluation, model internals, inference infrastructure, backend systems, and product interfaces. The goal is not simply to reproduce research. It is to turn promising methods into reliable, measurable, and usable products.\nWhat You’ll Do\nReproduce and evaluate research methods using open-weight and API-accessible models.\n\nDesign evaluation datasets, probes, scoring methods, baselines, calibration tests, and experiment harnesses.\n\nWork directly with model weights, logits, hidden states, activations, model APIs, and inference infrastructure when required.\n\nBuild and extend our evaluation infrastructure, including runners, judges, persistence, experiment orchestration, and reporting.\n\nTurn research workflows into product experiences, including experiment configuration, runs, traces, comparisons, reports, and review workflows.\n\nInvestigate how verification methods behave under model modification, including fine-tuning, merging, quantization, distillation, safety removal, and deliberate evasion.\n\nDesign controlled experiments that separate meaningful signals from artifacts or confounders.\n\nWrite clear technical reports that distinguish measured evidence, interpretation, and hypotheses.\n\nShip production-quality systems with APIs, background jobs, observability, testing, and documentation.\n\nWhat We’re Looking For\nStrong Python engineering skills and hands-on experience with PyTorch and Hugging Face Transformers.\n\nA strong understanding of ML evaluation, including dataset design, baselines, metrics, calibration, false positives, false negatives, statistical uncertainty, and reproducibility.\n\nAbility to read ML research papers and implement methods from first principles rather than relying entirely on existing packages.\n\nExperience building production software beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, and deployment.\n\nComfort working with open-weight models and understanding how modern LLM inference systems operate.\n\nAbility to work across backend and frontend boundaries. Our product surface is primarily React/TypeScript, and you should be able to make complex experiments and results understandable to users.\n\nStrong technical judgment about what experimental evidence does and does not support. For example, evidence that one model was derived from another is not necessarily evidence that it was directly trained on that model's outputs.\n\nHigh agency and a strong sense of ownership. You are comfortable identifying problems, proposing solutions, and driving work forward without waiting for detailed instructions.\n\nComfortable working in a fast-moving startup environment where priorities can evolve quickly and individuals are expected to operate across functions.\n\nUseful Experience\nExperience in any of the following is a plus:\nModel provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluations, or interpretability.\n\nActivation and representation analysis, probing, model hooks, logits, hidden states, or other model-internals work.\n\nEvaluation and inference infrastructure such as DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL/pgvector, or similar systems.\n\nNext.js, React, TypeScript, data visualization, or experiment dashboards.\n\nRunning and serving open-weight models on GPUs and reasoning about latency, throughput, memory, precision, and cost tradeoffs.\n\nDesigning adversarial evaluations or testing systems against deliberate attempts to evade detection.\n\nWhat Success Looks Like in the First Six Months\nYou will:\nReproduce at least one published model-provenance or verification method and clearly document its capabilities, assumptions, and limitations.\n\nBuild a repeatable model-verification runner with versioned inputs, artifacts, metrics, and reports.\n\nAdd at least one verification workflow to Construct and make it accessible through the Eldros UI.\n\nRun controlled experiments across base models, fine-tuned models, merged models, quantized models, and known distilled models.\n\nImprove our ability to understand when verification methods succeed, when they fail, and why.\n\nLeave behind production-quality code, tests, tooling, and documentation that another engineer can confidently operate and extend.\n\nThis Role Is Not\nA pure research role where work ends with a paper or notebook.\n\nA generic model-training or fine-tuning position.\n\nA frontend-only or backend-only engineering role.\n\nA role where benchmark scores are accepted at face value without understanding how they were produced.\n\nA role for someone who wants to stay within a single layer of the stack.\n\nWe are looking for someone who enjoys moving between research, experimentation, engineering, and product, and who cares about building systems that produce evidence people can actually trust.","description_format":"text","description_chars":5246,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"China","iso":"CN","kind":"region"},{"name":"India","iso":"IN","kind":"region"},{"name":"Japan","iso":"JP","kind":"region"},{"name":"United 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Lanka","iso":"LK","kind":"region"},{"name":"Tokelau","iso":"TK","kind":"region"},{"name":"Tonga","iso":"TO","kind":"region"},{"name":"Tuvalu","iso":null,"kind":"region"},{"name":"Vanuatu","iso":"VU","kind":"region"},{"name":"Wallis and Futuna","iso":"WF","kind":"region"},{"name":"Hong Kong","iso":"HK","kind":"country"},{"name":"Singapore","iso":"SG","kind":"country"},{"name":"Austin","iso":null,"kind":"city"},{"name":"Macau","iso":"MO","kind":"region"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-23T16:59:56Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":23,"reasons":["conf:2","win:early"],"computed_at":"2026-09-24T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/sentient-applied-ml-engineer","json_url":"https://alion.io/job/sentient-applied-ml-engineer.json","meta":{"generated_at":"2026-09-24T08:40:11Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}